Minimum Temperature Research Articles

Performance Evaluation of CO2 + SiCl4 Binary Mixture in Recompression Brayton Cycle for Warm Climates

This work demonstrates the potential of CO2 + SiCl4 binary mixture as a working fluid for power generation cycle. Recompression Brayton cycle configuration is considered due to its proven record of high performance for medium- to high-temperature sources. The objective of this study is to assess the thermodynamic performance of a recompression Brayton cycle using a CO2 + SiCl4 binary mixture as a working fluid, particularly under warm climate conditions. The cycle is simulated using the Peng–Robinson equation of state in Aspen Hysys (v11) software, and the model is validated by comparing VLE data against experimental data from the literature. The analysis involves the assessment of cycle’s thermal efficiency and exergy efficiency under warm climatic conditions, with a minimum cycle temperature of 40 °C. The results demonstrate a notable improvement in the cycle’s thermodynamic performance with CO2 + SiCl4 binary mixture compared to pure CO2. A small concentration (5%) of SiCl4 in CO2 increases the thermal efficiency of the cycle from 41.7% to 43.4%. Moreover, irreversibility losses in the cooler and the heat recovery unit are significantly lower with the CO2 + SiCl4 binary mixture than with pure CO2. This improvement enhances the overall exergy efficiency of the cycle, increasing it from 62.1% to 70.2%. The primary reason for this enhancement is the substantial reduction in irreversibility losses in both the cooler and the HTR. This study reveals that when using a CO2 + SiCl4 mixture, the concentration must be optimized to avoid condensation in the compressor, which can cause physical damage to the compressor blades and other components, as well as increase power input. This issue arises from the higher glide temperature of the mixture at increased SiCl4 concentrations and the limited heat recovery from the cycle.

Performance analysis of natural draft power plant cooling tower: Mathematical modeling of cross-flow type, application in capital cities of Iranian provinces

Cooling towers are heat exchangers that reduce the hot water temperature produced by process equipment through heat and mass transfer between water and air stream. This study investigates the performance of cross-flow cooling towers under various geometrical, physical, and environmental conditions. For this purpose, thermodynamic modeling of the cross-flow cooling tower was conducted, and the governing equations were numerically solved using the finite difference method. Unlike previous studies, the effects of water droplet deformation into an elliptical shape during its fall along the cooling tower were also considered. This deformation, which influences the drag coefficient, Reynolds number of the water droplets, droplet surface area, and its cross-section area, along with the incorporation of buoyancy force in the droplet momentum equation, led to an improvement in the results. Consequently, the maximum computational error in this case was reduced to 1.97 %, compared to the scenario where droplet deformation and buoyancy force were neglected. The change in geometrical parameters of the tower in Tehran and Rasht was investigated, revealing that increasing the outlet radius of the tower from 20 m to 22.5 m decreased the outlet water temperature by 1.21 °C and 0.67 °C and increased the thermal efficiency of the tower by 4.94 % and 2.88 % respectively, while increasing the tower height from 100 m to 120 m, only decreased the outlet water temperature by 0.47 °C and 0.26 °C, and increased the thermal efficiency of the tower by 1.89 % and 1.09 % respectively. Additionally, the cooling tower performance was assessed in the centers of different provinces .of Iran. Results showed that with an initial droplet radius of 1 mm and an inlet water temperature of 50 °C, the maximum and minimum temperature reductions occurred in Shahrekord by 20.16 °C and Bandarabbas by 12.37 °C, respectively.

Trends in Temperatures in Latin America: A Time-Series Perspective Based on Fractional Integration

Abstract In this article, we examine the time-series properties of the temperatures in Latin America. We look at the presence of time trends in the context of potential long-memory processes, looking at the average, maximum, and minimum values from 1901 to 2021. Our results indicate that when looking at the average data, there is a tendency to return to the mean value in all cases. However, it is noted that in the cases of Guatemala, Mexico, and Brazil, which are the countries with the highest degree of integration, the process of reversion could take longer than in the remaining countries. We also point out that the time trend coefficient is significantly positive in practically all cases, especially in temperatures in the Caribbean islands such as Antigua and Barbuda, Aruba, and the British Virgin Islands. When analyzing the maximum and minimum temperatures, the highest degrees of integration are observed in the minimum values, and the highest values are obtained again in Brazil, Guatemala, and Mexico. The time trend coefficients are significantly positive in almost all cases, with the only two exceptions being Bolivia and Paraguay. Looking at the range (i.e., the difference between maximum and minimum temperatures), evidence of orders of integration above 0.5 is found in nine countries (Aruba, Brazil, Colombia, Cuba, Ecuador, Haiti, Panama, the Turks and Caicos Islands, and Venezuela), implying that shocks in the range will take longer to disappear than in the rest of the countries.

Associations of heat with diseases and specific symptoms in Flanders, Belgium: An 8-year retrospective study of general practitioner registration data

IntroductionGlobal temperature rise has become a major health concern. Most previous studies on the impact of heat on morbidity have used hospital data. ObjectiveThis study aimed to quantify the association between ambient temperature and a variety of potentially heat-related medical conditions and symptoms using general practitioner (GP) data, in Flanders, Belgium. MethodsWe used eight years (2012–2019) of aggregated data of daily GP visits during the Belgian summer period (May-September). A distributed lag nonlinear model (DLNM) with time-stratified conditional quasi-Poisson regression was used to account for the non-linear and delayed effect of temperature indicators (minimum, mean and maximum). We controlled for potential confounders such as particulate matter, humidity, and ozone. ResultsThe overall (lag0-14) association between heat and most of the outcomes was J-shaped, with an increased risk of disease observed at higher temperatures. The associations were more pronounced using the minimum temperatures indicator. Comparing the 99th (20 °C) to the minimum morbidity temperature (MMT) of the minimum temperature distribution during summer, the relative risk (RR) was significantly higher for heat-related general symptoms (RR = 1.30 [95 % CI: 1.07, 1.57]), otitis externa (RR = 4.87 [95 % CI:2.98, 7.98]), general heart problems (RR = 2.43 [95 % CI: 1.33, 4.42]), venous problems (RR = 2.48 [95 % CI:1.55, 3.96]), respiratory complaints (RR = 1.97 [95 % CI: 1.25, 3.09]), skin problems (RR = 3.26 [95 % CI: 2.51, 4.25]), and urinary infections (RR = 1.37 [95 % CI: 1.11, 1.69]). However, we did not find evidence for heat-related increases in gastrointestinal problems, cerebrovascular events, cardiovascular events, arrhythmia, mental health problems, upper respiratory problems and lower respiratory problems. An increased risk of allergy was observed when the minimum temperature reached 17.8 °C (RR = 1.50 [95 % CI: 1.23, 1.83]). Acute effects of heat were observed (largest effects at the first few lags). SummaryOur findings indicated that the occurrence of certain symptoms and illnesses during summer season is associated to high temperature or environmental exposures that are augmented by elevated temperatures. Overall, unlike hospitalization data, GP visits data provide broader population coverage, revealing a more accurate representation of heat-health association.

Temporal change in minimum mortality temperature under changing climate: A multicountry multicommunity observational study spanning 1986–2015

Background: The minimum mortality temperature (MMT) or MMT percentile (MMTP) is an indicator of population susceptibility to nonoptimum temperatures. MMT and MMTP change over time; however, the changing directions show region-wide heterogeneity. We examined the heterogeneity of temporal changes in MMT and MMTP across multiple communities and in multiple countries. Methods: Daily time-series data for mortality and ambient mean temperature for 699 communities in 34 countries spanning 1986–2015 were analyzed using a two-stage meta-analysis. First, a quasi-Poisson regression was employed to estimate MMT and MMTP for each community during the designated subperiods. Second, we pooled the community-specific temporally varying estimates using mixed-effects meta-regressions to examine temporal changes in MMT and MMTP in the entire study population, as well as by climate zone, geographical region, and country. Results: Temporal increases in MMT and MMTP from 19.5 °C (17.9, 21.1) to 20.3 °C (18.5, 22.0) and from the 74.5 (68.3, 80.6) to 75.0 (71.0, 78.9) percentiles in the entire population were found, respectively. Temporal change was significantly heterogeneous across geographical regions (P < 0.001). Temporal increases in MMT were observed in East Asia (linear slope [LS] = 0.91, P = 0.02) and South-East Asia (LS = 0.62, P = 0.05), whereas a temporal decrease in MMT was observed in South Europe (LS = −0.46, P = 0.05). MMTP decreased temporally in North Europe (LS = −3.45, P = 0.02) and South Europe (LS = −2.86, P = 0.05). Conclusions: The temporal change in MMT or MMTP was largely heterogeneous. Population susceptibility in terms of optimum temperature may have changed under a warming climate, albeit with large region-dependent variations.

Hourly temperature downscaling method based on clustering and linear transformation: Utilizing mean, maximum, and minimum temperatures

Hourly temperature downscaling method based on clustering and linear transformation: Utilizing mean, maximum, and minimum temperatures

Increased offspring size and reduced gestation length in an ectothermic vertebrate under a worst-case climate change scenario

As global temperatures continue to rise, understanding the impacts of warming environments has become increasingly important. Temperature is especially relevant for ectothermic organisms which depend upon consistent and predictable annual temperature cycles for reproduction and development. However, additional research is required in this area to elucidate the potential impacts of climate change on future generations. To understand how projected increases in environmental temperatures may impact reproductive outcomes within natural populations of ectothermic vertebrates, we manipulated minimum ambient temperatures during gestation in Red-sided garter snakes (Thamnophis sirtalis parietalis). Wild snakes were collected in the Interlake region of Manitoba, Canada during their spring mating season and allowed to mate in controlled conditions. For the duration of gestation, mated females were placed into one of two ambient thermal conditions: temperatures emulating those found in the species’ natural habitat or temperatures with a consistent 5 °C increase to match end-of-century climate change projections. We recorded observations for each litter and all neonates resulting from controlled mating trials. We observed no difference in litter sizes or birth rates between thermal conditions. However, we observed a significant reduction in gestation length and significant increase to neonate body mass and body condition associated with increased ambient temperatures. These results suggest that increased minimum temperatures during gestation may confer reproductive benefits for the northern populations of this species even under the most extreme current modeled warming predictions. We discuss the broader implications of this effect, including possible negative ecological outcomes.

Machine learning-based climate zoning and asphalt selection for pavement infrastructure under changing climate: A focused study of Ningxia, China

Climate change poses significant challenges to the durability and performance of asphalt pavements. This study presents a comprehensive analysis of climatic factors in Ningxia, China, to establish a robust climate zoning framework for asphalt pavements. Utilizing machine learning techniques, specifically the Fuzzy C-means (FCM) algorithm, three distinct climate zones within Ningxia were divided considering climatic features such as maximum temperature, minimum temperature, average temperature, maximum temperature difference, cumulative precipitation, and cumulative radiation. Based on the historical climate data and LTPP model, five asphalt PG zones were classified in Ningxia Province. Besides, six climate sub-zones, which integrated the asphalt PG zones into climate zones, provided a more refined strategy for the asphalt selection. The study also projected future climate scenarios using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset to assess the impact of climate change on asphalt selection in Ningxia. The significant changes in pavement temperature indicated the necessity to adapt asphalt pavement designs to future climate scenarios. Overall, this research contributed to the construction of more climate-resilient pavement infrastructures and provided an analysis framework for other regions facing similar climate-induced challenges.

Distributional response of the rare and critically endangered Ilex nanchuanensis to climate change in East Asia

Abstract Global climate change, dominated by climate warming, is seriously affecting the balance of global ecosystems, but the risk of species extinction is particularly high in low-altitude mountain areas. To clarify the response of the endemic and critically endangered species Ilex nanchuanensis to climate change, this study used the MaxEnt model to simulate and predict the potential habitat of I. nanchuanensis during the Last Interglacial, Last Glacial Maximum, the current period, and two future periods (the 2050 s and 2070 s). The results showed that the hottest monthly minimum temperature is the most important climatic factor affecting the geographical distribution of I. nanchuanensis. Furthermore, I. nanchuanensis will be at risk of population shrinkage and extinction in the future, with the center of mass moving further northwest as concentrations of greenhouse gases increase, especially in the 2070 s, when its geographical distribution shrinks the most under the RCP6 scenario. Therefore, to actively respond to the impacts of climate change, protected areas should be established around the geographical distribution centers of species, and core, buffer, and experimental areas should be scientifically and rationally delineated for the conservation and cultivation of germplasm resources.

Inter–Intraseasonality of Meteorological Drivers of Chorabari Glacier, Central Himalaya: Implications for Mass Fluctuations and Associated Hazards

Abstract High-altitude meteorological data are scarce in the Himalayan region, particularly for glacierized catchments. In-depth knowledge of meteorological variables is crucial to describe numerous cryospheric processes and associated hazards. The present study comprehensively assesses time series data (2013–16) of meteorological parameters in the Chorabari Glacier catchment (CGC) using an automatic weather station (∼4270 m MSL). Results revealed that average near-surface air temperature varied seasonally, ranging from 6.4°C in the summer monsoon (June–September) to −6.3°C in the winter season (December–February). The minimum temperature (Tmin) remained positive throughout the monsoon seasons in the higher elevations (∼5050 m MSL), indicating the melting of glaciers and their reduced mass. The daily relative humidity in different seasons fluctuated from 39% to 90%, whereas the highest seasonal average (∼63%) was observed in the monsoon season. The average wind speed was high (∼1.7 m s−1) during winter and dropped to 1.1 m s−1 in the monsoon season. The average daily influx of solar radiation ranged from 19 to 374 W m−2, with the highest value recorded during the premonsoon (March–May ∼ 212 W m−2) and the lowest during the winter (∼149 W m−2) season. The daily outflux solar radiation varied from 18 to 186 W m−2, with the maximum value in premonsoon (∼145 W m−2) and the minimum value in the monsoon season (∼30 W m−2). The mean zero-degree isotherm was found above the equilibrium line altitude (ELA ∼ 5120 m MSL) during the monsoon season, indicating a considerable mass depletion of the Chorabari Glacier. The abnormal variations in climatic variables on 16–17 June 2013 were signs of the Kedarnath disaster. Significance Statement The main objective of this study is to understand the variability of meteorological drivers at daily, monthly, and seasonal scales and how these changes may impact glacier mass and downstream hydrology. Our research revealed considerable variations in maximum temperature throughout the year and positive minimum temperature at higher elevations during monsoon months, which significantly control the glacier mass changes. Meteorological variables showing cyclic behavior and indicators of the “monsoon lowering phenomenon” are important in determining the timing and intensity of the seasonal shift. Investigation of abnormal changes in the climatic drivers in the 2013 Kedarnath disaster provides valuable insight into the glacier-related hazards and draws attention to decision-makers to create robust strategies to monitor the dynamic response of Himalayan glaciers.

Development of a thermal diagnostic system for the SPARC tokamak.

The SPARC tokamak will have scrape-off layer parallel heat fluxes on the order of GW/m2. Managing power exhaust of this magnitude will be mandatory for a reactor-scale device. To enable this mission, a thermal diagnostic suite will be deployed to measure the in-vessel structural temperatures to ensure they do not exceed their design limits and to determine the spatial distribution and magnitude of energy deposited onto the first wall. Thermocouples and fiber Bragg gratings have been selected for their environmental compatibility and proven useful on other fusion devices. High-density thermocouple arrays in the divertor will have two spring-loaded thermocouples per divertor target tile, which are being used as calorimeters, and will look to resolve the temperature distribution within the tile due to a swept or static strike point. All systems will need to survive the vacuum vessel bake, set at a minimum plasma facing surface temperature of 350 °C, which presents a particularly challenging environment for the fiber-based subsystem. Along with this temperature design requirement, all the materials in the primary vacuum need to be ultra-high vacuum compatible, able to handle the expected neutron and gamma radiation, as well as tritium exposure, all of which restrict material options. Finally, due to the expected activated environment in SPARC, there will be little chance to replace defective sensors, so system resilience is ensured through toroidal redundancy, probe material selection, and mitigating the impact of common-mode failures. Initial testing of sensors show that intershot structural measurements are sufficiently captured with the raw output, but intrashot measurements of the plasma facing material requires model-based interpretive tools.

Increased exposure of rice to compound drought and hot extreme events during its growing seasons in China

Against the backdrop of global warming, compound drought and hot extreme events (CDHEEs) have intensified markedly, attracting attention from both the scientific community and society. However, a comprehensive understanding of the characteristics of the CDHEEs and their impacts on rice production during growing seasons remains limited. Thus, in this study, we use daily maximum and minimum air temperatures to derive a meteorological drought composite index for CDHEE identification. Additionally, we employ the Crop Calendar and the CROPGRIDS data to define the rice-growing seasons and regions. Detailed analyses reveal that from 1961 to 2023, the CDHEE frequency in both single-cropping rice regions (Rice1) and double-cropping rice regions (Rice2) increased at rates of 0.79 days (decade)−1 and 1.26 days (decade)−1, respectively, with a notable acceleration in the post-1990 s. The duration of the CDHEEs has lengthened, and prolonged events have become more frequent. The intensity of the CDHEEs has also enhanced, primarily driven by hot extreme events in Rice1 and the synergistic effects of droughts and hot extreme events in Rice2. Furthermore, the time span of CDHEE occurrence has expanded, starting earlier (rates of − 3.21 days (decade)−1 in Rice1 and − 4.37 days (decade)−1 in Rice2) and ending later (rates of 4.81 days (decade)−1 in Rice1 and 8.20 days (decade)−1 Rice2) during rice-growing seasons, thereby extending the exposure duration of rice. Since the 1990 s, rice exposure to the CDHEEs has also intensified, especially for the persistent CDHEEs. These findings indicate the upward trends in the frequency, intensity and duration of the CDHEEs, as well as the exposure of rice to the CDHEEs, during the rice-growing seasons in China. This research provides crucial insights into CDHEE impacts and underscores the necessity of implementing targeted adaptation and mitigation strategies to safeguard rice production amidst escalating climate challenges.

Clustering Anomalous Circulations and Their Implications on the Prediction of Minimum 2-m Air Temperature over Northeastern China during Winter

Abstract The ERA5 reanalysis during cold months (November–March) of 1979–2020 was used for determining four cluster centroids through the k-means for classifying regional anomalies of the daily geopotential height at 500 hPa (H500) over northeastern China. Empirical orthogonal function (EOF) was used to reduce dimensionality. Four clusters were linked to the EOF patterns with clear meteorological meanings, which are associated with the evolution of ridges and troughs over northeastern China. Those systems relate to warm and cold advections at 850 hPa. In each H500 cluster, the advection is the major contributor leading to temperature changes at 850 hPa, which significantly relates to the changes and anomalies of daily minimum air temperature at 2 m (T2min). Furthermore, the jet activities over Asia relate to more or less occurrence of specific H500 clusters in jet phases. This is because anomalous westerlies are generally in favor of positive anomalies of the vorticity tendency at 500 hPa. For the reforecasts during 2004–19 in the Chinese Meteorology Administration (CMA) S2S model, the hit rates above 50% for all the H500 clusters are within 9.5 days, which are in between those for the first two and the last two clusters. The correct prediction of H500 anomalies improves the T2min prediction up to 12 days, compared with 8 days for the incorrect one. The good prediction of the jet activities leads to a more accurate prediction of H500 anomalies. Therefore, improvement of the model prediction of jet activities and H500 anomalies will lead to better prediction of winter weather near the ground over northeastern China.

Vulnerability to heat-related mortality and the effect of prevention measures: a time-stratified case-crossover study in Switzerland.

Swiss climate scenarios predict increases in the frequency and intensity of extreme heat episodes in the future. For the effective prevention of heat-related mortality, several aspects of the population's vulnerability to heat must be understood on a local level. A nationwide analysis of individual death records was conducted, enabling a more comprehensive understanding than typical heat studies based on aggregated data. A total of 320,306 individual death records from the Swiss National Cohort with precise address information during the warm season (May to September) from 2003-2016 were linked to indoor and outdoor high-resolution daily temperature estimates. A time-stratified case-crossover study combined with distributed lag non-linear models was then performed to assess the temperature-mortality associations for various causes of death and to estimate the potential effect modification of individual characteristics. Additionally, it was explored whether the effect of extreme heat changed over time in regions with and without cantonal heat-health action plans (HHAPs). Using the temperature with the lowest cause-specific mortality risk (minimum mortality temperature) as the reference temperature, extreme heat (defined as ambient daily maximum temperature reaching 33 °C) was associated with a strong increase in all-cause mortality (odds ratio (OR): 1.21, 95% CI: 1.17-1.25) and disease-specific mortality from Alzheimer's disease and dementia (OR: 1.67, 95% CI: 1.48-1.88), COPD (OR: 1.37, 95% CI: 1.12-1.67), diabetes (OR: 1.34, 95% CI: 1.06-1.70), and myocardial infarction (OR: 1.26, 95% CI: 1.10-1.44). Indoor temperatures above 24 °C were found to be critical for mortality. The population most vulnerable to heat included older adults (≥75 years), unmarried individuals, people with a low education level, older women with low neighbourhood socioeconomic position, and men under 75 years old with low socioeconomic position. Overall, the risk of heat-related all-cause mortality in 2009-2016 was lower than that in 2003-2008. The decrease was significantly stronger in the region where cantonal HHAPs were implemented. This study provides important information for planning targeted and effective measures to reduce heat-related health risks in Switzerland. It demonstrates that HHAPs contribute to reducing heat-related mortality, although they may not reach the high-risk population of individuals with low socioeconomic position. Future prevention efforts should also target the less privileged population, including people younger than 75 years.

Modeling of ecological niches of Barbary Partridge (Alectoris barbara) under conditions of bioclimatic variability in the Fes-Meknes region (Morocco)

Abstract The Barbary Partridge (Alectoris barbara) is a bird species belonging to the Phasianidae family and is endemic to North Africa, including Morocco. Effective conservation of this species requires a thorough understanding and accurate assessment of the environmental factors that influence reproductive parameters. In this study, we used the species distribution models (SDM) to study the geographical distribution of the Barbary Partridge in Morocco. These models are tools that allow us to study the effects of climate change on the spatial distribution of species, they are based on the Maxent (maximum entropy) algorithm. These models are based on environmental and biological data and can predict the probability of a species’ occurrence in a given geographic area. They can also predict how climatic and environmental conditions will change over time and how these changes will affect the species’ distribution. The results obtained revealed that the environmental factors that have a significant influence on the distribution area of Barbary Partridge under Moroccan conditions are: annual precipitation (Bio12) with a contribution rate of 23.3%; precipitation of the driest quarter (Bio17) at 19.9%; altitude at 9.9%; minimum temperature of the coldest month (Bio 6) at 7.6%; precipitation of the driest month (Bio14) at 7.2%; and mean temperature of the wettest quarter (Bio8) at 4.4%. This information can be used to assist in long-term conservation planning by identifying areas that may become more or less suitable for the species.

Predator-specific mortality of sage-grouse nests based on predator DNA on eggshells.

Greater sage-grouse (hereafter sage-grouse; Centrocercus urophasianus) populations have declined across their range. Increased nest predation as a result of anthropogenic land use is one mechanism proposed to explain these declines. However, sage-grouse contend with a diverse suite of nest predators that vary in functional traits (e.g., search tactics or hunting mode) and abundance. Consequently, generalizing about factors influencing nest fate is challenging. Identifying the explicit predator species responsible for nest predation events is, therefore, critical to understanding causal mechanisms linking land use to patterns of sage-grouse nest success. Cattle grazing is often assumed to adversely affect sage-grouse recruitment by reducing grass height (and hence cover), thereby facilitating nest detection by predators. However, recent evidence found little support for the hypothesized effect of grazing on nest fate at the pasture scale. Rather, nest success appears to be similar on pastures grazed at varying intensities. One possible explanation for the lack of observed effect involves a localized response by one or more nest predators. The presence of cattle may cause a temporary reduction in predator density and/or use within a pasture (the cattle avoidance hypothesis). The cattle avoidance hypothesis predicts a decreased probability of at least one sage-grouse nest predator predating sage-grouse nests in pastures with livestock relative to pastures without livestock present during the nesting season. To test the cattle avoidance hypothesis, we collected predator DNA from eggshells from predated nests and used genetic methods to identify the sage-grouse nest predator(s) responsible for the predation event. We evaluated the influence of habitat and grazing on predator-specific nest predation. We evaluated the efficacy of our genetic method by deploying artificial nests with trail cameras and compared the results of our genetic method to the species captured via trail camera. Our molecular methods identified at least one nest predator captured predating artificial nests via trail camera for 33 of 35 (94%) artificial nests. We detected nest predators via our molecular analysis at 76 of 114 (67%) predated sage-grouse nests. The primary predators detected at sage-grouse nests were coyotes (Canis latrans) and corvids (Corvidea). Grazing did not influence the probability of nest predation by either coyotes or corvids. Sagebrush canopy cover was negatively associated with the probability a coyote predated a nest, distance to water was positively associated with the probability a corvid predated a nest, and average minimum temperature was negatively associated with the probability that either a coyote or a corvid predated a nest. Our study provides a framework for implementing an effective, non-invasive method for identifying sage-grouse nest predators that can be used to better understand how management actions at local and regional scales may impact an important component of sage-grouse recruitment.

Projected changes in mean climate and extremes from downscaled high-resolution CMIP6 simulations in Australia

High-resolution climate change projections are required to understand local and regional climate change impacts. We used CCAM (Conformal Cubic Atmospheric Model) to dynamically downscale CMIP6 GCMs (Global Climate Models) over Australia under three emissions scenarios, producing a set of 60 simulations at a 10 km resolution. Previous work has evaluated the performance of the downscaled models in the historical period. Here, we evaluate the impact of end-of-century climate change in the downscaled CMIP6-CCAM models for mean and extreme climate under three Shared Socioeconomic Pathways (SSP126, 245 and 370). We find the changes in mean climate are similar in the host CMIP6 and downscaled models. For extreme temperature, we find that extreme maximum temperatures (TXx) increase by 3.4°C, while extreme minimum temperatures (TNn) warm by 3.0°C. Extreme precipitation generally increases in summer and decreases in winter; however, there is a large amount of inter-model variation in the location and magnitude of change. Consecutive dry days also decrease in most areas in Austral summer and increase in Austral winter. Heatwaves become more frequent and hotter by the end of the century. These results suggest a hotter, wetter Austral summer, with longer, more frequent and more intense heatwaves, and a hotter and drier Austral winter in most areas. This dataset provides useful new high-resolution information on how climate change is likely to impact Australia, which will be a valuable resource to underpin local adaptation responses to future impacts.

Predicting the Potential Distribution of Rare and Endangered Emmenopterys henryi in China Under ClimateChange.

Climate change has a pivotal impact on the potential distribution of endangered and relic tree species. Probably due to unrepresentative sampling and single algorithm, at present, there are different views on the potential range of the endangered tree Emmenopterys henryi, endemic to China. Here, we first collated 612 occurrence records and 22 environmental variables including climate, topography, and soil. Combined the Biomod2 with MaxEnt, we then predicted its past, current, and future potential suitable area in China, and determined the key factors influencing its distribution. The ensemble model results showed that the main environmental variables affecting this species were the minimum temperature of the coldest month (BIO6), precipitation of warmest quarter (BIO18), and temperature seasonality (BIO4). Its current potential distribution area was 176.53 × 104 km2, mainly concentrated in eastern, central, and southwestern China. Collectively, the suitable area of E. henryi would averagely decrease by 3.90% in all 16 future scenarios, with its centroid largely migrating northeastward. Our findings indicate that the endangered E. henryi covered 18 provinces in China, having a larger area than known. Moreover, climate change may have an adverse effect on its potential distribution. In addition, the ensemble model can produce more effective prediction outcomes than MaxEnt for such endemic tree species with large environmental range. We recommend increasing sample representativeness by analyzing the completeness properties of sample coverage, and simultaneously selecting appropriate algorithms to ensure the reliability of distribution prediction for endangered and relict tree species.

The risk of hospitalization associated with hot nights and excess nighttime heat in a subtropical metropolis: a time-series study in Hong Kong, 2000–2019

Recent studies showed increased mortality risks after hot nights, but their effect on hospitalizations, especially in vulnerable populations, remains under-studied. Daily hospitalization, meteorological (including hourly), and air pollution data were collected for the hot seasons (May-October) of 2000-19 in Hong Kong. We derived three hot-night metrics: HNday28 °C, daily minimum temperature ≥28°C, the governmental definition of hot nights; HNe, hot night excess calculated by summing heat excess of hourly temperatures above 28°C at night; and HNday90th, hot nights classified using the 90th percentile HNe (17.7°C⋅h) as a cutoff. We fitted time-series regression with distributed lag nonlinear models to examine the associations of hot-night metrics with various hospitalizations. During the 3680 study days, 5,002,114 non-cancer non-external (NCNE) hospitalizations were recorded. Half (1874) of the days experienced excess nighttime heat (HNe>0) with a mean (SD) of 8.0 (6.8) °C⋅h; 499 and 187 hot nights were identified by HNday28 °C and HNday90th, respectively. Extreme HNe (99th percentile vs 0°C⋅h) was significantly associated with increased NCNE hospitalizations over lag 0-4 days by 3.1% [95% confidence interval: 1.5%, 4.8%] overall, with enhanced effects in elderly (5.3% [3.2%, 7.4%]), low-SES individuals (5.3% [2.8%, 8.0%]), and circulatory admissions (3.4% [0.2%, 6.8%]). HNday90th, reflecting extreme HNe, better identified hazardous hot nights than the official HNday28 °C. Excessive nighttime heat is significantly associated with increased hospitalizations, particularly affecting the elderly and socioeconomically disadvantaged individuals. Nighttime heat intensity should be incorporated in defining hot nights with public health relevance. British Heart Foundation.

A century of medical records reveal earlier onset of the malaria season in Haut-Katanga induced by climate change

BackgroundDespite worldwide efforts to eradicate malaria over the past century, the disease remains a significant challenge in the Democratic Republic of the Congo (DRC) today. Climate change is even anticipated...