Daily Air Temperature Research Articles

Effect of the uncertainty of autocorrelation estimation on trend analysis

Autocorrelation is ubiquitous in real-life time series. For trend analysis, positive autocorrelation mainly increases the statistical uncertainty of the detected trend. The theoretical framework has been established to quantify how the autocorrelation can impact trend analysis if its characteristic is known precisely. However, if the autocorrelation is also uncertain like that estimated in real-life data, then the consequences are yet unclear. The situation is further complicated by the concurrence of both short- and long-range correlation (SLRC), e.g., in air temperature. In this study, we use an appropriate minimal model with one short-range autoregressive parameter and one long-range fractional parameter to account for such SLRC, and then accordingly propose a framework to integrate the effect of the uncertainty of autocorrelation estimation into trend analysis, on the basis of the detrended fluctuation analysis and Akaike weights. This framework can also obtain a joint probability density function of the estimated autoregressive and fractional parameters for measuring their uncertainty, identifying their combined optimal estimates, uncovering their strong interdependence, and determining their two-dimensional confidence region. The effectiveness and applicability of the proposed framework are verified by a numerical experiment and a case study of daily air temperature anomalies at the Potsdam station. This study can provide a solid theoretical basis to underpin our understanding of the autocorrelation effect on trend analysis in theory and applications. Published by the American Physical Society 2024

Spatio-temporal trends in the frost regime reveal late frost exposure to white spruce (Picea glauca [Moench] Voss) persists in northeastern America

Abstract The characteristics of the frost regime (intensity, frequency, and timing) contribute to shaping tree species adaptations and distribution as well as ecosystem productivity and functions. However, climate change increases the variability in extreme events; therefore, the different characteristics of the frost regime may diverge under climate change. Using the BioSim 11 software, we simulated daily air temperature at 512 locations over northeastern North America between 1901–2021 to determine how the spatio-temporal trends in the frost regime varied over this complex landscape and if spatio-temporal trends in extreme climatic events such as frosts are stronger compared to changes in aggregated climate variables such as mean annual air temperature and growing degree-days. We also used an eco-physiological model to conduct a case study focussing on white spruce to determine if trees are currently more exposed to growing season frosts than they were in the past by modelling the timings of budburst using the thermal time model. Our results showed that, at 67% of locations (343 locations), the day of the year of the last frost in spring (minimum daily air temperature<0 °C) occurred, on average, earlier by seven days during 2001–2021 compared to 1901–1920, whereas it occurred, on average, later by four days at 33% of locations (169 locations). The average temporal trends in frost occurrence were similar in magnitude to the average trends in aggregated climate variables; however, their variances were larger compared to the aggregated climate metrics, showing that the frost regime does not change uniformly throughout our study area. Our case study also revealed that white spruce remains exposed to late frosts of low and intermediate intensities (minimum daily air temperature <0; <−2 °C) compared to the past but exposure to high-intensity frosts (minimum daily air temperature <−4 °C) is rare. Since extreme events such as late frosts diverge in their response to climate change compared to aggregated climate variables, the mean annual temperature is not sufficient to predict how climate change will impact ecosystems through frost regimes.

Study of red vine phenotypic plasticity across central-southern Italy sites: an integrated analysis of the transcriptome and weather indices through WGCNA

The grapevine (Vitis spp., family Vitaceae) is characterized by marked phenotypic plasticity. Its ability to withstand specific environmental conditions depends on the activation of highly coordinated responses resulting from interactions among genotypes (G) and environmental factors (E). In this study, the transcriptomes of commercially ripe berries of the Cabernet Sauvignon and Aglianico genotypes grown in open fields at three different sites in central-southern Italy (Campania, Molise and Sicily) were analyzed with RNA sequencing. These transcriptomic data were integrated with a comprehensive set of weather course indices through weighted gene co-expression network analysis (WGCNA). A total of 11,887 differentially expressed genes (DEGs) were retrieved, most of which were associated with the Aglianico genotype. The plants from the Sicilian site presented the greatest number of DEGs for both genotypes. Most of the weather course data (daily maximum air temperature, relative humidity, air pressure, dew point, and hours of sun radiation) were significantly correlated with the “lightcyan1” module, confirming WGCNA as a powerful method for identifying genes of high biological interest. Within this module, the gene encoding the ACA10 cation transporter was highly expressed in plants of both genotypes from Campania, where the lowest anthocyanin content was recorded. The transcriptome was also correlated with quality traits, such as total soluble solids and polyphenol content. This approach could lead to the identification of a transcriptomic profile that may specifically identify a genotype and its growing site and to the discovery of hub genes that might function as markers of wine quality.

Global record-breaking recurrence rates indicate more widespread and intense surface air temperature and precipitation extremes.

We analyzed the evolution of extreme annual surface air temperature and rainfall on Earth, based on the recurrence rate of record-breaking events, and found the highest recurrence rates for record-high annual temperatures in the tropics, as opposed to the polar regions with the fastest warming. Both recurrence rates and the global surface area fraction with daily mean surface air temperatures exceeding 30° and 40°C provide further evidence for extremely hot years becoming more common and widespread. A similar analysis for precipitation highlighted some regions with more record-high annual total precipitation and others with record-low annual precipitation typically associated with drought. A multimodel ensemble of 306 runs with global climate models [Coupled Model Intercomparison Project phase 6 (CMIP) Shared Socioeconomic Pathways 2-45 (SSP2-45)] reproduced the statistics of record-breaking high temperatures, but there were some differences for the reanalysis precipitation record-breaking recurrence rates. The global climate model simulations suggested a slightly altered geographical pattern for record-breaking annual precipitation recurrence rates.

Daylength Influences Time to Harvest and Stem Quality of Pycnosorus globosus and Caryopteris incana Specialty Cut Flowers

As the demand for locally grown specialty cut flowers increases, cultivars of Pycnosorus globosus and Caryotperis incana are becoming more available for growers to produce as cuts. However, protocols for year-round greenhouse production are limited. Therefore, our objective was to determine if the photoperiod influences flower induction and development, time to harvest, yield, and morphology of billy buttons (Pycnosorus globosus) ‘Paintball Globe’ and ‘Paintball Poppy’ and bluebeard (Caryopteris incana) ‘Pagoda Lagoon’ and ‘Pagoda Dark Pink’ to facilitate commercial greenhouse production. Shoot-tip cuttings were rooted and transplanted into bulb crates and placed in a greenhouse with a mean daily air temperature of 20 °C and 9-, 10-, 11-, 12-, 13-, 14-, 15-, or 16-hour photoperiods or a 9-hour short day (SD) with a 4-hour night interruption (NI) from 2200 to 0200 HR for 11 weeks. Billy buttons initiated inflorescences and developed flowers under all daylengths; however, the greatest stem lengths were recorded under long days (LDs). Bluebeard inflorescences initiated under all daylengths were tested. Time to visible inflorescence and flower was hastened under daylengths ≤14 hours, whereas inflorescences under the 16-h photoperiod or 4-h NI never fully developed. For ‘Pagoda Lagoon’ and ‘Pagoda Dark Pink’, the critical daylengths for flower development were 14 hours and 15 hours, respectively. On average, only bluebeard ‘Pagoda Lagoon’ harvested under photoperiods ≥10 hours developed marketable stem lengths (>40 cm). To hasten time to visible inflorescence and harvest and ensure marketable stem lengths, billy buttons should be grown under daylengths ≥13 hours. Based on these results, we recommend growing bluebeard under a 16-hour photoperiod or NI for 4 to 6 weeks to promote vegetative growth, followed by an 11- to 14-hour photoperiod for flower induction and development. We classify billy buttons as a day-neutral plant for flower induction and a facultative LD plant for flower development. Similarly, bluebeard can be classified as a facultative SD plant for flower induction and an obligate SD plant for flower development.

Effect of High Nighttime Temperatures on Growth, Yield, and Quality of Two Wheat Cultivars During the Whole Growth Period.

It is a consensus that Earth's climate has been warming. The impact of global warming is asymmetric, that is, there is more substantial warming in the daily minimum surface air temperature and lower warming in the maximum surface air temperature. Previous studies have reported diurnal temperature differences greatly affecting winter wheat yield. However, only a few studies have investigated the impact of global warming on the growth and yield of winter wheat, yet the influence of night warming on quality has not been deeply evaluated. In this study, two wheat cultivars were used as materials: Jimai 44 (JM44) with strong gluten and Jimai 22 (JM22) with medium gluten, to explore the effects of high nighttime temperatures (HNTs) on the growth, yield, and quality of wheat. The results show that HNTs significantly shortened seedling emergence and anthesis periods in both cultivars compared with ambient temperatures (ATs). In addition, HNTs increased the respiration rate at anthesis and grain-filling stages, impeding wheat pollination and grain maturity. HNTs also accelerated leaf senescence and increased the number of sterile spikelets and plant height, but decreased the effective tiller number, the number of spikes per unit area, and grains per spike. As a result, the grain yield of JM22 and JM44 was decreased by 24.6% and 21.2%, respectively. Moreover, HNTs negatively influenced the flour quality of the two wheat cultivars. The current findings provide new insights into the effects of HNTs on the growth, development, yield, and quality of different wheat genotypes during the whole growth period.

Effects of short-term exposures to lower air temperature and cold spells on myocardial infarction hospital admissions: a nationwide study in Sweden

Abstract Background Exposures to lower air temperature and cold spells have been associated with an increased risk of various diseases. However, the short-term effect of lower air temperature and cold spells on myocardial infarction (MI) remains incompletely understood. Purpose We aimed to evaluate the short-term effects of lower air temperature and cold spells on MI hospital admissions within the nationwide SWEDEHEART registry. Methods This population-based nationwide study included 120,380 MI cases admitted to hospitals in Sweden during the cold season (October to March) from 2005 to 2019. Daily mean air temperature was estimated at 1 km2 spatial resolution across Sweden using a three-stage machine learning approach and percentiles of daily temperatures experienced by individuals in the same municipality were employed as individual exposure indicators to account for potential geographic adaptation. Cold spells were defined as periods of at least two consecutive days with a daily mean temperature below the 10th percentile of the temperature distribution for each municipality. A time-stratified case-crossover design incorporating a conditional logistic regression model with distributed lag non-linear model using lag 0-1 and lag 2-6 days was employed to evaluate the short-term effects of lower air temperature and cold spells on total MI, non-ST-segment elevation MI (NSTEMI) and ST-segment elevation MI (STEMI). Results We found significant increases in risk for total MI, NSTEMI, and STEMI in association with a decrease of one percentile in temperature at a lag of 2-6 days, indicated by ORs (95% CI) of 1.099 (1.057-1.142), 1.110 (1.06-1.164), and 1.076 (1.004-1.153), respectively. Additionally, we found that cold spells were significantly associated with increased risks for total MI, NSTEMI, and STEMI at a lag of 2-6 days, with ORs (95% CI) of 1.077 (1.037-1.12), 1.069 (1.02-1.119), and 1.095 (1.023-1.172), respectively. Moreover, independent effects of lower air temperature and cold spells on MI occurrence were found after adjusting for each other. Conversely, we observed that lower air temperature and cold spells were associated with decreased risks for MI at a lag of 0-1 days. Conclusions This nationwide case-crossover study demonstrated a novel biphasic pattern in MI risk following exposure to lower air temperatures and cold spells, characterized by an initial transient reduction in risk at lags 0-1 day, followed by a significant increase in risk at lags 2-6 days. As climate change continues to induce complex shifts in regional weather patterns, enhancing protections to reduce future cold-related cardiac hospitalizations is crucial.

Statistical-empirical pavement temperature prediction models based on data from road weather stations in Estonia

Asphalt pavement properties are greatly affected by seasonal temperature variation. Binder grade selection needs to be aligned with forecasted operational pavement temperatures to maximise pavement longevity. Different widely adopted models for determining binder Superpave Performance Grading have been shown to provide inaccurate results for the Nordic-Baltic region, leading to under- or overestimation of operational pavement temperatures and erroneous selection of a suitable binder grade. This study proposes empirically formulated pavement temperature prediction models for Estonia based on temperature data collected from six different road weather stations in Estonia from 2019 to 2023. Models were derived using measurements from daily maximum and minimum air temperatures combined with pavement temperature profiles with depth.

Application of meta-heuristic hybrid models in estimating the average air temperature of Caspian sea coast of Iran

The rise of industrial societies leads to higher greenhouse gas emissions, profoundly affecting the climate in coastal regions. Consequently, air temperature readings from standard meteorological stations are key indicators of the Earth's environmental condition. Therefore, accurate estimation of daily temperature in each region is one of the important prerequisites for agricultural planning as well as water resources management and drought prevention, which can be done in different ways such as experimental, semi-experimental and intelligent models. In this research, WSVR, AIG-SVR, GWO-SVR and BAT-SVR hybrid models were investigated and evaluated in order to estimate the average daily air temperature on the shores of the Caspian Sea located in the north of Iran. For modeling, weather station data from Babolsar meteorological station located in Mazandaran province were used. During the water year from 2012 to 2022, daily parameters including relative humidity, maximum temperature, minimum temperature, wind speed, and evaporation were selected as network inputs, with the average daily air temperature as the network output. To assess and compare model performances, several criteria were employed including correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percentage bias. Comparative analysis revealed that the WSVR model surpassed other models, demonstrating the highest correlation coefficient (0.992), lowest RMSE (0.096), and lowest MAE (0.042). The highest Nash Sutcliffe criterion (0.996) and bias percentage (0.001) were prioritized in the validation stage.

Construction of a comprehensive meteorological risk index for wheat Fusarium head blight prediction based on more than a half-century of monitoring data.

Fusarium head blight (FHB) represents a critical threat to wheat production globally, not only reducing yields but also contaminating crops with harmful mycotoxins. This study aimed to elucidate new spatiotemporal patterns of FHB incidence and to develop a comprehensive meteorological risk index to enhance scientific prevention and control of the disease. Through the analysis of annual and decadal variations from 1965 to 2023, the study assessed FHB trends across four agricultural regions (I, II, III, and IV) in Jiangsu Province, located in the middle and lower reaches of the Yangtze River-a hotspot for FHB in China. Key findings include: Since 1965, Regions I and III consistently exhibited higher FHB incidence rates compared to Regions II and IV. Post-2000, there was a notable increase in years with high incidence rates, with Region III overtaking Region I as the region with the highest incidence. Since 2010, occurrences of FHB reaching the most severe grade (Grade 5) have surpassed those in previous decades across all regions. The study also revealed a stronger correlation between meteorological factors (cumulative precipitation, number of days with rainfall ≥ 0.1 mm, total rainy days with ≥ 2 and ≥3 consecutive days of rain, total rainy days with both average daily air temperature ≥ 15 °C and daily rainfall ≥ 0.1 mm, days with average daily relative humidity ≥ 85%, cumulative sunshine hours, and cumulative cloudy days) and the FHB incidence rates during the heading-flowering-grain filling period in Regions I, II, and III, compared to the heading-flowering period alone. This indicates that optimal temperature and high humidity during the grain filling stage significantly contribute to the final FHB incidence rates. Despite the less apparent correlation between temperature changes and disease rates, the significant warming trend observed since 2000 has likely fostered conditions conducive to the proliferation of FHB. The comprehensive meteorological risk index, constructed to incorporate key meteorological factors during the heading-flowering-filling period, showed a strong correlation with actual disease incidences. The index demonstrated fitting accuracy rates of 84.7% for Region I, 72.9% for Region II, 83.1% for Region III, and 90.9% for Region IV, underscoring its effectiveness in predicting FHB occurrences. This tool offers both convenience and practicality, providing valuable insights for strategically managing FHB risks based on local weather conditions.

Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022

A high-accuracy, continuous air temperature (Ta) dataset with high spatiotemporal resolution is essential for human health, disease prediction, and energy management. Existing datasets consider factors such as elevation, latitude, and surface temperature but insufficiently address meteorological and spatiotemporal factors, affecting accuracy. Additionally, no high-resolution dataset currently includes daily maximum (Tmax), minimum (Tmin), and mean (Tmean) temperatures generated using a unified methodology. Here, we introduce the four-dimensional spatiotemporal deep forest (4D-STDF) model, integrating 12 multisource factors, encompassing static and dynamic parameters, and six refined spatiotemporal factors to produce Ta datasets. This approach generates three high-accuracy Ta datasets at 1 km spatial resolution covering mainland China from 2003 to 2022. These datasets, in GeoTIFF format with WGS84 projection, comprise daily Tmax, Tmin, and Tmean. The overall RMSE are 1.49 °C, 1.53 °C, and 1.18 °C for the estimates. The 4D-STDF model can also be applied to other regions with sparse meteorological stations.

Remotely Sensed Estimation of Daily Near-Surface Air Temperature: A Comparison of Metop and MODIS

Remotely Sensed Estimation of Daily Near-Surface Air Temperature: A Comparison of Metop and MODIS

Analysing the influence of temperature dependence of active resistances on transmitted power in electric power systems

Subject of research: in this paper, the static stability of electric power systems is assessed taking into account the temperature dependence of active resistances. Purpose of research: to show how the consideration of the temperature dependence of active resistances will affect the static stability reserves of electric power systems. Methods and objects of research: calculations of the regime with account of influence of thermal processes in elements of power system are made. The main parameter to take into account thermal processes is the ambient air temperature, so the days with maximum and minimum average daily air temperature were determined. All parameters except ambient air temperature and wind speed were selected in such a way as to provide the worst conditions for thermal exchange. The results of the calculations show that taking into account the thermal processes in the elements of the system significantly affects the transmitted power and the reserves of static aperiodic stability in the power system. Main results of research: practical calculations have shown that the account of thermal processes allows to take into account more accurately the limits of the transmitted power through the elements of the power system, and at the same time the reserve coefficients of static stability.

Machine learning reveals dynamic controls of soil nitrous oxide emissions from diverse long-term cropping systems.

Soil nitrous oxide (N2O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003-2019) of measurements at the Kellogg Biological Station Long-Term Ecological Research (LTER)/Long-Term Agroecosystem Research (LTAR) site to better understand the controls of N2O emissions in four corn-soybean-winter wheat rotations employing conventional, no-till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily N2O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2-day precipitation, water-filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%-42% of daily N2O flux variability in the test data, with greater predictability for the corn phase in each system. The long-term rotations showed different controlling factors and threshold conditions influencing N2O emissions. In the conventional system, the model identified ammonium (>15 kg N ha-1) and daily air temperature (>23°C) as the most influential variables; in the no-till system, climate variables such as precipitation and air temperature were important variables. In low-input and organic systems, where red clover (Trifolium repens L.; before corn) and cereal rye (Secale cereale L.; before soybean) cover crops were integrated, nitrate was the predominant predictor of N2O emissions, followed by precipitation and air temperature. In low-input and biologically based systems, red clover residues increased soil nitrogen availability to influence N2O emissions. Long-term data facilitated machine learning for predicting N2O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.

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.

Less power when more is needed. Climate-related current and possible future problems of the wind energy sector in Poland.

Heat waves, combined with an increasing share of weather-dependent renewable energy sources, lead to disruptions in the operation of energy systems. This paper estimates how energy consumption in Poland changed during four heat waves (2010-2022). It also analyzes how often wind conditions may occur in the summer, that interrupt energy production by land-based wind farms. Moreover, authors are looking at future changes of climate indices responsible for increased energy consumption and possible reduction of energy production in wind farms. During the 2010 heatwave, the increase in energy consumption during the hottest days did not exceed 4%. Later summer demand increased; in 2019, it was 8% higher than during the 2015 heatwave. In 2016-2022, energy consumption at the average daily air temperature above 26°C was over 8% higher on working days than at 18°C. Recently, even in regions with favorable wind conditions, the number of summer days with low wind speed (2 ms-1) exceeds 14. For Poland, the number of days with low wind speeds will increase by about ten. Therefore, the increasing demand for energy on hot days, combined with their more frequent occurrence, may have a negative impact on the stability of the energy system in the future.

Study of the feature of the temperature regime of the Mykolaiv and region in the context of the issue of climate change

Climate change is one of the most important and acute environmental problems of our time. It includes a complex of changes, such as rising ambient temperatures, changes in precipitation patterns, rising sea levels, and more frequent extreme weather events. According to the Intergovernmental Panel on Climate Change (IPCC), the average global temperature has risen by about 1.1°C since the late nineteenth century. Projections point to a possible increase of 1.5°C by the middle of this century unless significant measures are taken to reduce greenhouse gas emissions. On the basis of the above, the paper analyzes trends in changes in atmospheric air temperature on the territory of risky agriculture in the South of Ukraine in the Mykolaiv region.The purpose of the study: to determine and substantiate the regularities of changes in the temperature regime of Mykolaiv and Mykolaiv region in the context of climate change.Research results. The values of temperature characteristics for the year and season in Mykolaiv and Mykolaiv region during 1991−2023 are analyzed. In Mykolaiv, about 60-67 days with a negative average daily air temperature and about 32 days with a negative maximum daily air temperature are recorded. The duration of the period with a negative minimum air temperature can be an average of 93 days per year: 64 in winter, 16 in spring and 14 in autumn. At the same time, on a large territory of the Mykolaiv region, autumn is warmer than spring. The average seasonal air temperature is 10.6°C, the average maximum temperature is 15.4°C, and the average minimum temperature is 6.4°C. In the Northern Black Sea region, both maximum and minimum temperatures in autumn are higher than in spring. The dynamics of changes in average annual temperatures in the city of Mykolaiv in the period from 1980 to 2023 are analyzed. A stable linear trend towards a gradual increase in average annual temperatures has been determined. The warmest year for this observation period is 2023, the coldest are 1985 and 1987. It is determined that the rate of increase in average temperature is 0.61°C for every ten years. Maximum temperatures are increasing at a rate of 0.0884°C per year and minimum temperatures are decreasing at a rate of 0.0136°C per year. Every year, the average annual temperature increases by approximately 0.021°С. The largest number of days accompanied by heat stress (up to 90%) is observed in July – August. Thus, changes in air temperature indicate a significant change in the temperature regime of the entire climate system of the region, and the consequences of climate change may be predominantly negative and will intensify in the future.

Short-Term Effects of Lower Air Temperature and Cold Spells on Myocardial Infarction Hospitalizations in Sweden

BackgroundLower air temperature and cold spells have been associated with an increased risk of various diseases. However, the short-term effect of lower air temperature and cold spells on myocardial infarction (MI) remains incompletely understood. ObjectivesThe purpose of this study was to investigate the short-term effects of lower air temperature and cold spells on the risk of hospitalization for MI in Sweden. MethodsThis population-based nationwide study included 120,380 MI cases admitted to hospitals in Sweden during the cold season (October to March) from 2005 to 2019. Daily mean air temperature (1 km2 resolution) was estimated using machine learning, and percentiles of daily temperatures experienced by individuals in the same municipality were used as individual exposure indicators to account for potential geographic adaptation. Cold spells were defined as periods of at least 2 consecutive days with a daily mean temperature below the 10th percentile of the temperature distribution for each municipality. A time-stratified case-crossover design incorporating conditional logistic regression models with distributed lag nonlinear models using lag 0 to 1 (immediate) and 2 to 6 days (delayed) was used to evaluate the short-term effects of lower air temperature and cold spells on total MI, non–ST-segment elevation myocardial infarction (NSTEMI) and ST-segment elevation myocardial infarction (STEMI). ResultsA decrease of 1-U in percentile temperature at a lag of 2 to 6 days was significantly associated with increased risks of total MI, NSTEMI, and STEMI, with ORs of 1.099 (95% CI: 1.057-1.142), 1.110 (95% CI: 1.060-1.164), and 1.076 (95% CI: 1.004-1.153), respectively. Additionally, cold spells at a lag of 2 to 6 days were significantly associated with increased risks for total MI, NSTEMI, and STEMI, with ORs of 1.077 (95% CI: 1.037-1.120), 1.069 (95% CI: 1.020-1.119), and 1.095 (95% CI: 1.023-1.172), respectively. Conversely, lower air temperature and cold spells at a lag of 0 to 1 days were associated with decreased risks for MI. ConclusionsThis nationwide case-crossover study reveals that short-term exposures to lower air temperature and cold spells are associated with an increased risk of hospitalization for MI at lag 2 to 6 days.

Habitat suitability and distribution patterns of Rouget's rail (Rougetius rougetii Guérin-méneville, 1843) in Ethiopia.

Geographical distribution and diversity patterns of bird species are influenced by climate change. The Rouget's rail (Rougetius rougetii) is a ground-dwelling endemic bird species distributed in Ethiopia and Eritrea. It is a near-threatened species menaced by habitat loss, one of the main causes of population declines for bird species. The increasing effects of climate change may further threaten the species' survival. So far, the spatial distribution of this species is not fully documented. With this study, we develop current potential suitable habitat and predict the future habitat shift of R. rougetii based on environmental data such as bioclimatic variables, population density, vegetation cover, and elevation using 10 algorithms. We evaluated the importance of environmental factors in shaping the bird's distribution and how it shifts under climate change scenarios. We used 182 records of R. rougetii from Ethiopia and nine bioclimatic, population density, vegetation cover, and elevation variables to run the 10 model algorithms. Among 10 algorithms, eight were selected for ensembling models according to their predictive abilities. The current suitable habitats for R. rougetii were predicted to cover an area of about 82,000 km2 despite being highly fragmented. The model suggested that temperature seasonality (bio4), elevation, and mean daily air temperatures of the driest quarter (bio9) contributed the most to delimiting suitable areas for this species. R. rougetii is sensitive to climate change associated with elevation, which leads shrinking distribution of suitable areas. The projected spatial and temporal pattern of habitat loss of R. rougetii suggests the importance of climate change mitigation and implementing long-term conservation and management strategies for this threatened endemic bird species.

Identifying groups at-risk to extreme heat: Intersections of age, race/ethnicity, and socioeconomic status

Anthropogenic climate change has resulted in a significant rise in extreme heat events, exerting considerable but unequal impacts on morbidity and mortality. Numerous studies have identified inequities in heat exposure across different groups, but social identities have often been viewed in isolation from each other. Children (5 and under) and older adults (65 and older) also face elevated risks of heat-related health impacts. We employ an intersectional cross-classificatory approach to analyze the distribution of heat exposure between sociodemographic categories split into age groups in the contiguous US. We utilize high-resolution daily air temperature data to establish three census tract-level heat metrics (i.e., average summer temperature, heat waves, and heat island days). We pair those metrics with American Community Survey estimates on racial/ethnic, socioeconomic, and disability status by age to calculate population weighted mean exposures and absolute disparity metrics. Our findings indicate few substantive differences between age groups overall, but more substantial differences between sociodemographic categories within age groups, with children and older adults from socially marginalized backgrounds facing greater exposure than adults from similar backgrounds. When looking at sociodemographic differences by age, people of color of any age and older adults without health insurance emerge as the most exposed groups. This study identifies groups who are most exposed to extreme heat. Policy and program interventions aimed at reducing the impacts of heat should take these disparities in exposure into account to achieve health equity objectives.