Mean Maximum Temperature Research Articles

Biochemical Studies on Evaluation of Sunflower (Helianthus annuus L.) Genotypes for Heat Stress

Crops are facing heat stress because of rapid climate change caused by global warming. We examined these issues in sunflower by exposing the plants growing with mean maximum temperature of 32.2oC (sowing to flowering) and 36.1oC (flowering to harvest) at normal temperature (S1) and 35.3oC (sowing to flowering) and 38.3 oC (flowering to harvest) at high temperatures (S2) by staggered sowings. Antioxidative responses of sunflower were also explored by studying the Superoxide dismutase, Catalase, Peroxidises and Ascorbate peroxidise activities. A significant increase was observed in antioxidant enzyme activities under high temperature stress. The final oil composition proved to be sensitive to the timing of heat stress and reduced (13%) by high temperatures. Some innovative steps should be taken on an emergency basis to prepare plants for such stressful conditions.

Projection on elevation-dependent and latitude-dependent warming over Antarctica from CMIP6 under different socioeconomic scenarios

Many studies have focused on elevation-dependent warming (EDW) across high mountains, but few studies have examined both EDW and LDW (latitude-dependent warming) on Antarctic warming. This study analyzed the Antarctic amplification (AnA) with respect to EDW and LDW under SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5 from Coupled Model Intercomparison Project Phase 6 (CMIP6) during the period 2015–2100. The results show that the AnA appears under all socioeconomic scenarios, and the greatest signal appears in austral autumn. In the future, Antarctic warming is not only elevation-dependent, but also latitude-dependent. Generally, positive EDW of mean temperature (Tmean), maximum temperature (Tmax) and minimum temperature (Tmin) appear in the range of 1.0–4.5 km, and the corresponding altitudinal amplification trends are 0.012/0.012/0.011 (SSP1–2.6)—0.064/0.065/0.053 (SSP5–8.5) °C decade−1•km−1. Antarctic EDW demonstrates seasonal differences, and is strong in summer and autumn and weak in winter under SSP3–7.0 and SSP5–8.5. For Tmean, Tmax and Tmin, the feature of LDW is varies in different latitude ranges, and also shows seasonal differences. The strongest LDW signal appears in autumn, and the warming rate increases with increasing latitude at 64–79°S under SSP1–2.6. The similar phenomenon can be observed at 68–87°S in the other cases. Moreover, the latitude component contributes more to the warming of Tmean and Tmax relative to the corresponding altitude component, which may relates to the much larger range of latitude (∼2600 km) than altitude (∼4.5 km) over Antarctica, while the EDW contributes more warming than LDW in the changes in Tmin in austral summer. Moreover, surface downwelling longwave radiation, water vapor and latent heat flux are the potential factors influencing Antarctic EDW, and the variation in surface downwelling longwave radiation can also be considered as an important influencing factor for Antarctic LDW. Our results provide preliminary insights into EDW and LDW in Antarctica.

A Proposed Two-stage Bayesian Modeling for Calibration of Epidemiological Forecasts with Applications on Dengue Cases in Butuan City

This paper presents a comparative analysis of modeling approaches for a high-frequency time series. The moving average of 567 morbidity weeks of dengue cases in Butuan City, a Highly Urbanized City (HUC), was used as the response variable, with the aggregated rainfall, mean maximum temperature, mean minimum temperature, and mean relative humidity as the set of regressors. The last 24 morbidity weeks were set to be used for validation of predictive accuracy. Some pieces of literature support the robustness of the use of Bayesian methods in drawing inferences, modeling, and predicting epidemiological data. Hence, candidate Bayesian Econometric models were applied following appropriate assumptions. The applicability of Bayesian Vector Autoregression (BVAR) for variable selection and lag inclusion purposes was empirically supported. The BVAR results show that the dependent variable was mostly sensitive only to the variabilities in both the (a) direct effects and (b) lags of the cases themselves, and rainfall. The generated lags as included regressors were used in a separate model using the Bayesian Metropolis-Hastings (BMH) Algorithm. For comparison, a Frequentist Vector Autoregression (FVAR) Model as the baseline model, and BMH Algorithm were applied, too. Predictions comparison shows that the variable and lag selection process of BVAR combined with the BMH Algorithm (BVAR-BMH) simulation resulted in promising gains in predictive accuracy against straightforwardly using FVAR, BVAR, or BMH algorithm for the original set of variables. The promising gains in predictive accuracy may be used in anticipatory actions for dengue epidemiological surveillance for the specified HUC, or other locations.

Climate and Wolbachia Impacts on Anoplolepis gracilipes (Hymenoptera: Formicidae).

The yellow crazy ant (Anoplolepis gracilipes (Smith, 1857)) is a prominent invasive species exhibiting variable population dynamics. Through collecting long-term climate data and validating field surveys with MaxEnt SDM projections, our results indicated that winter temperature and precipitation accumulation strongly influence the population dynamics. An aggression analysis showed that A. gracilipes nests with higher aggression levels (over 2.5 scores) experienced a higher mean maximum temperature (31.84 ± 0.43 °C) and lower prevalence of wAgra (84.8 ± 4.70%) in A. gracilipes from June to October. The nest manipulation and aggression experiments confirmed that temperature increases aggression (1.3 to 2.8 scores) among A. gracilipes workers due to the reduced prevalence of wAgra. To the best of our knowledge, this is the first case of a notable reduction in the prevalence of Wolbachia (100 to 66%) within a colony of A. gracilipes while maintaining stable nests for further experiments.

Vertical characteristics of temperature and relative humidity during the spring continuous warming processes in Altay, Xinjiang of China

To understand the triggering conditions of snowmelt floods in northern Xinjiang, China, the diurnal evolution and altitude dependence characteristics of spring temperature and relative humidity (RH) are investigated, particularly placing emphasis on continuous warming process, mainly based on the data collected from the densely sounding experiment at Altay Station in spring 2021. The results show that the mean maximum and minimum temperature respectively occur at nightfall and sunrise, the mean RH below 2,000 m is the highest at sunrise and the lowest at nightfall, and the mean RH above 5,000 m is the highest at sunrise and the lowest at midday. The mean RH value climbs up first but goes down later, and the largest mean RH is within the height range of 2,500–5,000 m. The radiation inversion with the depth of 660 m tends to occur frequently at midnight and around sunrise, and it does not exist when the continuous warming process sets out but gradually develops with the advancing process. During the continuous warming processes, there exists an unsynchronized warming from the ground to the upper air. The upper-air warming lags behind that on the ground for 2–5 d, and then it continues the trend. Below 2,000 m, accompany with the advance of the warming processes, the RH keeps at relatively low level and changes small along height after the first 2–3 days, when the max RH drop swiftly from 80%–98% to less than 25% and tend to be consistent at the four observation times. The trough and ridge systems responsible for the continuous warming processes in early and middle spring are mainly the deep long-wave system, but in the middle and late spring, the adjustment speed of the trough and ridge systems are accelerated and more short-wave activities fuel the continuous warming.

Dam Sustainability’s Interdependency with Climate Change and Dam Failure Drivers

The complex interplay between the drivers of dam failure and climate change factors constitutes a central focus of this study. Variations in temperature and precipitation patterns have profound effects on dam storage volume, quality, and structural stability. This research serves two purposes. First, it employs the Wroclaw Taxonomic Method (WTM) to construct a comprehensive indicator called the Index of Sustainable Dams (ISD). The King Talal Dam (KTD) was chosen as a case study using data spanning from 2011 to 2022, encompassing 14 variables. Second, the Granger Causality Test (GCT) is applied to examine the causal connections between the ISD, climate change, and dam failure. The research provides an in-depth evaluation of the KTD in terms of the ISD and its three critical dimensions: environmental, economic, and social. It also reveals substantial bidirectional causality relationships between the ISD, climate change (as measured by mean maximum and minimum temperature and mean annual precipitation), and dam failure (as measured by dam age, sediment yield, and storage). The analysis confirms a strong interdependence among the drivers of climate change, dam failure, and dam sustainability. Moreover, combining the WTM and the GCT is efficient for assessing the sustainability of various dam types.

Modeling the impact of climate change on streamflow responses in the Kessem watershed, Middle Awash sub-basin, Ethiopia

Abstract In this study, we examined how future climate change will affect streamflow responses in the Kessem watershed. Climate variables from SSP2-4.5 and SSP5-8.5 emission scenarios were extracted from GCMs for the 2040s (2031–2060) and 2070s (2061–2090). The bias-corrected precipitation and temperature were converted into streamflow using a calibrated SWAT model. The simulated output of the future streamflow for the periods 2040s and 2070s was compared with the base period (1992–2020) and presented as percentage changes. During calibration and validation, the SWAT model showed Nash–Sutcliffe efficiency (NSE) values of 0.79 and 0.77, as well as coefficient of determination (R2) values of 0.8 and 0.79, demonstrating its capability of simulating streamflow. The annual mean maximum and minimum temperatures are predicted to increase, with a pronounced increase in the minimum temperature for the mid-term and long-term futures under both emission scenarios. As we approach the end of the century, we see an increase in annual mean rainfall and streamflow under the SSP5-8.5 emission scenario. The increment in annual mean rainfall (streamflow) is expected to be 3% (12.5%) and 23% (48.8%) for the 2040s and 2070s, respectively, under the SSP5-8.5 emission scenario.

Effects of climate factors on wheat and maize under different crop rotations and irrigation strategies in the North China Plain

The North China Plain (NCP) is a crucial agricultural region for grain production in China, primarily focusing on wheat and maize cultivation. However, these crop yields are highly vulnerable to fluctuations in temperature and precipitation, with climate change being a significant factor. This study investigates the impact of climate factors on wheat and maize yields in the NCP under various crop rotations and water supply strategies. Using the Agricultural Production Systems sIMulator crop mechanism model, we evaluated the effects of different crop rotations and water supply strategies on wheat and maize yields. A comprehensive analysis of the simulated results determined the yield variation trends and their correlation and sensitivity to different climate factors. The findings revealed that precipitation levels over the past 40 years showed no significant trend, while radiation levels showed a significant decreasing trend, and annual mean maximum and minimum temperatures displayed significant increasing trends. Furthermore, the study found that irrigation practices and crop rotations substantially impact grain yield in the study area, with average yields ranging from 8105.5 kg ha−1 under rainfed conditions to 13 088.8 kg ha−1 under fully irrigated conditions. There was a statistically significant trend of increasing yields for fully irrigated Monocrop-Wheat and decreasing yields for fully irrigated Monocrop-Maize over the past 40 years. Sensitivity analysis also showed that rational rotation and irrigation can reduce grain yield sensitivity to climate change. In conclusion, the prudent use of rotation and irrigation can enhance food production resilience to climate change. These findings have significant implications for developing strategies to optimize crop yields and adapt to climate change in the NCP while considering trade-offs.

Associations between ambient temperature and suicide: а systematic review

BACKGROUND: Over the past 150 years, a large number of articles have been published around the world on the associations between ambient temperature and suicide. However, only in the last 30 years daily data with sufficient validity are in use, necessitating the systematization of the latest research evidence.
 AIM: To conduct a systematic selection and qualitative synthesis of published information on the associations between ambient air temperature and suicide from 1990.
 MATERIALS AND METHODS: A systematic review using PRISMA criteria. Studies published in peer-reviewed journals from January 1990 to April 2022 in PubMed and eLIBRARY in Russian or English, respectively, comprised the study base.
 RESULTS: Of the 1932 identified articles, 32 empirical studies with the number of cases ranging from 225 to 1,067,333 from 26 countries were included in the qualitative synthesis. In addition to mean temperatures, the minimum and maximum temperatures, as well as the calculated apparent mean, minimum and maximum temperature were used. In some studies, the difference in temperature between the index and the previous day were used. By design, most studies had ecological or case-crossover design. Generalized Additive Models (GAM) were used as the main analytical tools, and in multicenter studies the results were obtained using meta-regression. The results were presented as relative risks or the percentage increase in the number of suicides with an increase in temperature by 1 C. Virtually all studies reported a significant linear relationship between the temperature and suicide rates, but effect sizes varied between countries. The effect was more pronounced in temperate climate, in men and people over 65 years of age.
 CONCLUSIONS: Most of the studied suggest a direct relationship between high air temperature and the number of suicides in different latitudes. It is necessary to monitor the situation and study the mechanisms behind the associations between high temperatures and the risk of suicide to minimize the consequences of climate change for public health.

Spatially Explicit Correction of Simulated Urban Air Temperatures Using Crowdsourced Data.

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP-BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies.

Forecasting Worldwide Temperature from Amazon Rainforest Deforestation Using a Long-Short Term Memory Model

Biosphere–atmosphere interactions are a critical component of the Earth’s climate system. Many of these interactions are currently contributing to temperature increases and accelerating global warming. One of the main factors responsible for this is land use and land cover changes; in particular, this work models the interaction between Amazon rainforest deforestation and global temperatures. A Long Short-Term Memory (LSTM) neural network is proposed to forecast temperature trends, including mean, average minimum, and average maximum temperatures, in 20 major cities worldwide. The Amazon rainforest, often referred to as the Earth’s “lungs”, plays a pivotal role in regulating global climate patterns. Over the past two decades, this region has experienced significant deforestation, largely due to human activities. We hypothesize that the extent of deforestation in the Amazon can serve as a valuable proxy for understanding and predicting temperature changes in distant urban centers. Using a dataset that tracks cumulative deforestation from 2001 to 2021 across 297 municipalities in the Amazon rainforest, a multivariate time series model was developed to forecast temperature trends worldwide up to 2030. The input data reveal a variety of behaviors, including complex deforestation patterns. Similarly, the forecasted temperature data showcases diverse trends. While some cities are expected to exhibit a steady temperature increase, others may experience gradual changes, while some cities may undergo drastic and rapid temperature shifts. Our findings contribute to a deeper understanding of the far-reaching impacts of deforestation on global climate patterns and underscore the importance of preserving vital ecosystems like the Amazon rainforest.

Proposed Econometric Strategy in Determining Thresholds of Levels of Diseases with Applications to Dengue Cases of a Highly Urbanized City

Disease thresholds of outbreaks are typically set at two standard deviations above the endemicity and are arbitrary in nature. For dengue, the potential effects of the lags in the rise of the reported number of cases, the complexity of the impacts of specific meteorological parameters, possible effects of climate change, and natural environmental anomalies, among others, also vary per geographical area which could further complicate the identification of disease thresholds. Common practice is to calculate the two standard deviations beyond the endemic level. Existing research typically focuses on calculating a reproduction number pertaining to the average number of people infected by a person with the infection in a population that is otherwise negative of the disease. This study, on the other hand, aimed to propose a novel approach by applying econometric procedures to identify thresholds of Levels of Disease that could be useful in drafting epidemiological surveillance guidelines and plans of action. The proposed strategy was done using the moving average (MA) of the weekly totals of confirmed dengue cases in Davao City in the Philippines, from January 2018 to December 2020, as the dependent variable. The study setting was selected since it is classified as a Highly Urbanized City, and hence, urbanization and globalization, which are among the primary drivers of dengue emergence, are observed. As regressors, meteorological variables, such as total rainfall, mean maximum temperature, mean minimum temperature, and relative humidity, which have been substantiated by the literature as widely utilized variables with varying impacts on the transmission of dengue cases, were used in this study. The proposed five-stage strategy includes (1) Optimal Lag selection, (2) Autoregressive Distributed Lag (ARDL) Model applicability assumption evaluation, (3) extracting the MA of the number of weeks identified as the optimal lag, (4) applying the ARDL Model to determine supplemental significant regressors and lags, and (5) implementing the Threshold Regression (TR) Model to determine the thresholds to differentiate the Levels of Disease for the study setting. The findings show the optimal lag is 2 weeks. The variables used were statistically proven to be a combination of stationary at the level and the first difference and hence, the ARDL Model was deemed applicable. Further, the ARDL Model retained (1) the first and (2) second lags of the dependent variable, as well as (3) the direct effects of mean maximum temperature. These three significant variables were used as fixed independent variables whereas the rainfall, minimum temperature, and humidity were used as region-varying regressors, for the dependent variable in the TR Model. Three Levels of Disease were recommended after optimally determining two threshold values which are 71.00 and 88.50. Hence, the three levels for the two-week MA of dengue cases are Endemic Level (below 71.00), Hyperendemic Level (71.00 to 88.50), and Outbreak Level (above 88.50) are recommended. This reproducible spatiotemporal strategy that combines epidemiology and econometrics can be applied to other geographical areas and diseases and be further improved with a longer time series.

Influence of climate factors on population density and damage of the leopard moth, Zeuzera pyrina L., in walnut orchards, Iran.

The effect of climate factors (temperature, humidity, precipitation, and frost days) on the population changes, damage, and infestation area of the leopard moth, Zeuzera pyrina L., was studied during 2006-2018 in four parts of Iran including Saman, Arak, Najaf-abad, and Baft. For trend analysis, the Mann-Kendall test was run on time series data of both climate and pest population. According to the results, the annual mean (Kendall's statistics, T = 0.64 and 0.48), annual minimum (T = 0.60 and 0.42), and January mean (T = 0.64 and 0.61, respectively) temperatures showed increasing trends in Saman and Najaf-abad. Moreover, the annual mean minimum and January temperatures (T = 0.41 and 0.45, respectively) in Arak and the annual mean maximum temperature (T = 0.79) in Baft showed increasing trends. The number of frost days/year (Kendall's statistics, T = -0.63, -0.53, -0.32 and -0.37) and annual mean relative humidity (T = -0.43, -0.63, -0.64 and -0.42, respectively) showed decreasing trends in Saman, Arak, Baft, and Najaf-abad stations. Trend analysis indicated significant increases in the mean number of moths caught (T = 0.59, 0.76 and 0.90), the percentage of infested branches/tree (T = 0.66, 0.58, and 0.90), the number of active holes/tree (T = 0.79, 0.55, and 0.68) and the infested areas (T = 0.99, 0.73, and 0.98, respectively) in Saman, Arak and Najaf-abad stations. According to stepwise regression, the mean temperatures of January, autumn, and winter were the most effective variables for increasing Z. pyrina damage and population, while relative humidity and the number of frost days played the major role in reducing it.

Tree Ring Width Responses of Pinus densiflora and Robinia pseudoacacia to Climate Variation in the Mount Tai Area of Northern China

To effectively combat climate change and put plans in place to enhance the health and quality of forests, research on the connections between long-term climate change and tree ring width is essential. Here, Pinus densiflora Siebold & Zucc. and Robinia pseudoacacia L. in the Mount Tai area were studied. Specifically, their tree ring width characteristics were investigated according to the principles of dendrochronology, based on the analysis of multiyear climate data (1972–2022). The results showed that (1) the variation in tree ring width of Pinus densiflora Siebold & Zucc. generally decreased. Its basal area increment sequence presented a “growth-decline” change process. The trend for Robinia pseudoacacia L. was stability at first and then a reduction. Its basal area increment sequence presented a “growth-stabilization” change process. (2) The standard chronologies of both tree species contained more environmental information than the residual chronologies, rendering the former more appropriate for analysis. (3) The tree ring width of Pinus densiflora Siebold & Zucc. was positively correlated with the mean maximum temperature in February, March, and July, the mean temperature in May, and the mean precipitation in December of the previous year and June of the current year. The tree ring width of Robinia pseudoacacia L. was positively correlated with the mean and maximum temperatures in October of the previous year, the mean maximum temperature in May of the current year, and the mean temperature in July of the current year. This variable was positively correlated with the mean precipitation in November of the previous year and July of the current year but negatively correlated with the mean minimum temperature in November of the previous year and the mean temperature in December of the current year. (4) During 1987–1988, 1994–1995, 2005–2006, and 2018–2019, the tree ring width was restricted by rising temperatures and low precipitation, with variations in tree ring width observed in various hydrological periods. (5) The moisture index, followed by the warmth index, precipitation and temperature, was the primary climate factor determining the tree ring width in both tree species.

Contrasting immediate impact of prescribed fires and experimental summer fires on soil organic matter quality and microbial properties in the forest floor and mineral soil in Mediterranean black pine forest

Prescribed fire (PB) is used to achieve ecological objectives and to reduce fuel hazard thus limiting detrimental impacts of wildfire and appropriate selection of prescription window is critical for these goals. Operational use of PB in the Mediterranean forest is scarce and information about its effects on soil remains incomplete. This study for the first time i) compared the immediate impact of spring and autumn PB and experimental summer fire on key properties of forest floor and mineral topsoil in Mediterranean black pine forest, and ii) assessed the capacity of PB to reduce fuel, with limited immediate impacts on soil. PB significantly reduced the 32.5 % of pre-fire forest floor depth, while summer fire consumed 88.5 % and exposed about 30 % of the mineral soil surface. Mean maximum temperature during fire at the mineral soil surface was 23 °C in PB, in contrast to 128 °C in summer fire, while soil heating at 2 cm depth was negligible in both cases. PB did not cause immediate changes in OM quality parameters, and chemical (C and N concentrations, C/N and pH) and microbiological properties (Cmic, Cmic/C, and β-glucosidase, acid phosphatase and alkaline phosphatase activities) in forest floor or mineral topsoil (0–2 cm). By contrast, summer fire greatly increased OM recalcitrance and reduced Cmic, Cmic/C and enzyme activities in forest floor immediately after fire. In the mineral topsoil, only microbial properties were significantly reduced. The maximum temperature reached during fire in forest floor and topsoil was associated with most of the overall changes in properties in both layers. The findings suggest that prescribed fire can significantly reduce fuel with limited initial impacts on soil. Although these findings are encouraging for operational use of prescribed burning in the ecosystem under study, long-term monitoring of repeated application of the technique on soil properties and other ecosystem components is necessary.

Emergent Constrained Projections of Mean and Extreme Warming in China

AbstractReliable regional temperature projections including heat extremes are essential for climate change adaptation and mitigation. Taking China as an example, simple averages from Coupled Model Intercomparison Project Phase 6 (CMIP6) models project high warming due to sampling many high climate sensitivities in the ensemble. Here, we develop an emergent constraint (EC) framework to obtain constrained mean and daily maximum temperature (TXx) warming over China by using observed global warming and local residual warming. The constrained annual mean and TXx warming over China (2.33°C [1.61–3.05°C] and 2.31°C [1.21–2.99°C]) are 0.65°C [0.04–1.76°C] and 0.63°C [–0.50–2.39°C], respectively, lower than raw projections (2.98°C [1.85–4.22°C] and 2.94°C [2.04–4.39°C]) for 2080–2099 under the intermediate‐emission scenario. Approximately half model uncertainty is reduced after constraint. The land area (population) experiencing temperature extremes in our metric is 78% (85%) of the raw projections. Our results imply a lower impact of extreme heat than implied by current raw CMIP6 projections.

Exploring temperature dynamics in Madhya Pradesh: a spatial-temporal analysis.

Understanding the dynamics of temperature trends is vital for assessing the impacts of climate change on a regional scale. In this context, the present study focuses on Madhya Pradesh state in Central Indian region to explore the spatial-temporal distribution patterns of temperature changes from 1951 to 2021. Gridded temperature data obtained from the Indian Meteorological Department (IMD) in 1° × 1° across the state are utilised to analyse long-term trends and variations in temperature. The Mann-Kendall (MK) test and Sen's slope (SS) estimator were used to detect the trends, and Pettitt's test was utilised for change point detection. The analysis reveals significant warming trends in Madhya Pradesh during the study period during specific time frames. The temperature variables, such as the annual mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin), consistently increase, with the most pronounced warming observed during winter. The trend analysis reveals that the rate of warming has increased in the past few years, particularly since the 1990s. However, Pettitt's test points out significant changes in the temperature, with Tmean rising from 25.46 °C in 1951-2004 to 25.78 °C in 2005-2021 (+0.33 °C), Tmax shifting from 45.77 °C in 1951-2010 to 46.24 °C in 2011-2021 (+0.47°C), and Tmin increasing from 2.65 °C in 1951-1999 to 3.19 °C in 2000-2021 (+0.46 °C). These results, along with spatial-temporal distribution maps, shed important light on the alterations and variations in monthly Tmean, Tmax, and Tmin across the area, underlining the dynamic character of climate change and highlighting the demand for methods for adaptation and mitigation.

Long-term air temperature trends in North Cyprus

The Eastern Mediterranean region is a major climate change hotspot. The island of Cyprus is likely to face increases in the frequency and intensity of hotter weather conditions and heatwaves in the near future. Studies conducted on the long-term temperature changes in Cyprus are very limited. Here, we present an updated and most detailed assessment of the maximum, minimum, mean and diurnal temperature series in North Cyprus for the period 1975–2021. Data obtained from the meterological stations of North Cyprus have been analysed using Mann–Kendal (MK) test and Theil–Sen slope estimator. Overall the mean minimum temperature trend (Tmin) showed the highest warming rate 0.61 (0.24 ≤ Tmin ≤ 0.99)°C decade−1 followed by the mean temperature trend (Tmean) 0.38 (0.29 ≤ Tmean ≤ 0.50) °C decade−1 and the mean maximum temperature trend (Tmax) 0.28 (0 ≤ Tmax ≤ 0.50) °C decade−1. The magnitude of the warming trend observed in the overall mean minimum temperature of North Cyprus 0.61 °C decade−1, is one of the fastest warming trends reported in the literature. A negative association was detected between the direction of prevailing winds of North Cyprus and the magnitude of increase in the mean temperature trends of the locations with coastal Mediterranean climate, which has pointed out the importance of prevailing winds regarding their cooling effect in coastal areas. The diurnal temperature range trend of North Cyprus indicates an apparent decrease (− 0.33 °C decade−1). The warming impact of urban heat island effect was detected in temperature trends of Nicosia in the Mesaoria plain. The information provided here is invaluable to consider in any climate assessment and adaptation plan in Cyprus. If the current warming trend persists into the future, it will devastatingly impact all sectors and natural systems in the region.

Coated Cast Saw Blades Decrease Blade Temperature During Cast Removal.

Thermal injuries can occur during cast removal with an oscillating saw. The purpose of this study is to describe the effect of blade material, blade wear, and cast material on blade temperature. Short arm plaster and fiberglass casts were cured overnight and bivalved with either new or worn stainless-steel or tungsten-disulfide coated blades. Blade use was defined as removing 1 short arm cast. Blades used to remove more than 20 casts were defined as worn. Ten trials were performed with each combination of blade material, blade wear, and cast material, except for the combination of fiberglass and a worn stainless-steel blade due to complete erosion of cutting teeth. Thirty casts were removed with stainless-steel blades, and 40 casts were removed with coated blades. Blade temperature was measured continuously at 5Hz using a T-type thermocouple and microcontroller board. A Mann-Whitney U test was used to compare the mean maximum temperature between groups. For plaster casts, new and worn stainless-steel blades produced mean maximum temperatures of 51.4°C and 63.7°C (P=0.003), respectively. New stainless-steel blades produced significantly higher mean maximum temperatures on fiberglass casts than plaster casts (95.9°C vs. 51.4°C, P<.001). For plaster casts, new and worn coated blades produced mean maximum temperatures of 56.9°C and 53.8°C (P=0.347). For fiberglass casts, new and worn coated blades produced mean maximum temperatures of 76.6°C and 77.7°C (P=0.653). As with new stainless-steel blades, new coated blades produced significantly higher mean maximum temperatures on fiberglass than plaster (76.6°C vs. 56.9°C, P<0.001). Mean maximum temperatures between new stainless-steel and coated blades during removal of plaster casts were 51.4°C and 56.9°C (P=0.131), respectively. However, new coated blades demonstrated significantly lower mean maximum temperatures during fiberglass cast removal compared to new stainless-steel blades (76.6°C vs. 95.9°C, P=0.016). Coated blades outperformed stainless-steel in nearly all combinations. We recommend limited use of stainless-steel blades to minimize blade temperatures during cast removal. Appropriate selection of blade material and monitoring blade wear minimizes blade temperature during cast removal.

Species range shifts of notorious invasive fish species in China under global changes: Insights and implications for management

Due to global changes, e.g., climate change and trade globalization, China is facing an increasingly severe threat from invasive freshwater fish species, which have the potential to cause negative impacts across various aspects and pose significant challenges for their eradication once established. Therefore, prioritizing the understanding of invasive species’ potential ranges and their determinants is vital for developing more targeted management strategies. Moreover, it is equally essential to consider the transitory range dynamics of invasive species that reflect changes in habitat availability and accessibility. Here, we used species distribution models (the maximum entropy algorithm) to assess the potential distributions of six notorious invasive fish species (i.e., Coptodon zillii, Cyprinus carpio, Gambusia affinis, Hemiculter leucisculus, Oreochromis mossambicus, and Oreochromis niloticus) in current and future (i.e., the 2030s, 2050s, and 2070s) periods along with their determinants, under two Shared Socio-economic Pathways scenarios (SSP1-2.6 and SSP5-8.5; global climate model: MRI-ESM2-0). Our results showed that the habitat suitability for the six species substantially benefited from temperature conditions (i.e., annual mean temperature or maximum temperature of warmest month). Throughout the given time periods, dramatic range expansions would occur for C. zillii, G. affinis, O. mossambicus, and O. niloticus, ranging from 38.61% to 291.90%. In contrast, the range of C. carpio would change slightly and irregularly, while H. leucisculus would contract marginally, with losses ranging from 1.06% to 12.60%. By the 2070s, species richness of these species would be relatively high in South, Central, and East China and parts of Southwest China. Furthermore, transitory fluctuations in the species ranges for all six species were observed throughout the entire time period (the 2030s–2070s). Given the range shifts for each species during different time periods, as well as time costs and budgets, adaptation strategies should be developed and implemented in the areas where they are most needed in each time period.