Occurrence Of Temperature Extremes Research Articles

Application of pervious concrete pavement in the "breathe in-breathe out" design for sponge cities in China.

Sponge city construction is an ideal approach to mitigate the degradation of urban water environments. Among road materials, permeable concrete pavement stands out due to its unique structure that allows rainwater runoff to flow through its pores. This paper analyzes the current application status and the prospect of different permeable pavement designs in China's sponge cities, aiming to offer valuable insights for urban planning and construction. Statistical analysis summarizes the spatial-temporal distribution patterns of urban flooding disasters in China and their causes. By comparing the characteristics and advantages of pervious concrete pavement with traditional concrete pavement, the potential of permeable concrete pavement in sponge city construction is summarized through case studies. The findings highlight that by adjusting the pore size, permeable concrete pavement can collect rainwater while filtering impurities, thereby purifying surface runoff. The range of the pervious coefficient should ideally fall within the range of 4~8 mm/s. In addition, the pavement's large contact area with the air and internal water evaporation contributes to its self-regulating capability, reducing the occurrence of extreme temperatures. Related experiments have shown that from 8 am to 12 pm, pervious concrete pavement can reduce the temperature by approximately 1 °C compared to conventional concrete. From 12 pm to 8 pm, this temperature difference increases to approximately 3 °C. To meet the needs of environmental protection and resource utilization, permeable concrete pavement can serve as an ideal tool to achieve green and low-carbon development.

A temporal analysis of the consequences of the drought regime on the water footprint of agriculture in the Guadalupe Valley, Mexico

Changes in water availability have a substantial impact on the sustainability and maintenance of agriculture, with water footprint (WF) being a robust methodology to assess these transformations. The Guadalupe Valley is one of the places with the highest agricultural production in Mexico. Despite its semi-arid climatic conditions, it provides high-quality crops that are well-positioned in the world. The historical trend of rainfall and temperatures between 1987 and 2017 was analyzed to identify climatic patterns in the territory. Through the calculations of the water footprint of Grapevine and Olive crops, the sensitivity of the crops to recurrent water deficit and their adaptation in their yields to drought episodes was identified. The reduction in precipitation and occurrence of extreme temperatures have contributed significantly towards augmenting crop evapotranspiration and, consequently, intensifying crop irrigation demands. As a result, there has been an apparent increase in the consumption of WFagricultural since 2007. Thus, the period of highest WFagricultural consumption was 2014 (Extremely dry), as opposed to 2011 (Very wet). In particular, the lowest WFgreen consumptions were observed in extremely dry years, that is, > 20% of the WFagricultural intensifying drought events. Therefore, these periods were compensated with higher uses of WFblue and WFgray, which are inversely correlated with precipitation, where vine crops consume 73% more WFagricultural compared to olive plantations, showing greater interannual variability. These results contribute to analyzing the temporal evolution of water consumption for agriculture, providing a basis for rational water use strategies.

A Machine Learning Approach for the Estimation of Alfalfa Hay Crop Yield in Northern Nevada

Increasing pressure over water resources in the western U.S. is currently forcing alfalfa (Medicago sativa L.) producers to adopt water-saving irrigation techniques. Crop yield forecasting tools can be used to develop smart irrigation scheduling methods that can be used to estimate the future effects of a given irrigation amount applied during a current irrigation event on yield. In this work, a linear model and a random forest model were used to estimate the yield of irrigated alfalfa crops in northern Nevada. It was found that water (rain + irrigation), the occurrence of extreme temperatures, and wind have a greater effect on crop yield. Other variables that accounted for the photoperiod and the dormant period were also included in the model and are also important. The linear model had the best performance with an R2 of 0.854. On the other hand, the R2 value for the random forest was 0.793. The linear model showed a good response to water variability; therefore, it is a good model to consider for use as an irrigation decision support system. However, unlike the linear model, the random forest model can capture non-linear relationships occurring between the crop, water, and the atmosphere, and its results may be enhanced by including more data for its training.

European Summer Synoptic Circulations and Their Observed 2022 and Projected Influence on Hot Extremes and Dry Spells

AbstractIn 2022, western Europe experienced its hottest summer on record and widespread dry conditions, with substantial impacts on health, water and vegetation. We use a reanalysis to classify daily mean sea level pressure fields and to investigate the influence of synoptic circulations on the occurrence of temperature extremes and dry days. Summer 2022 featured an above‐normal occurrence of anticyclones extending from the British Isles to the Baltic countries, as well as enhanced easterly, southerly and low‐flow conditions which contributed to the observed extremes over southern and western Europe. While the hot summer of 2022 is only partially explained by circulation anomalies, such anomalies played a key role in the exceptional occurrence of dry days. The comparison with summer circulation anomalies projected by twenty global climate models moreover suggests that future circulation changes will further exacerbate hot and dry extremes over Europe.

Development of personal comfort model and its use in the control of air conditioner

The accurate identification of the real-time thermal state of personnel working indoors has been the focus of researchers. In this study, a detailed examination of the entire process of developing a personal comfort model, from theory to practice, is conducted. In particular, the subject of this investigation is a smart air conditioner capable of predicting thermal sensation based on an infrared (IR) sensor that measures the skin temperature of personnel and then implements automatic control. Random forest algorithm was employed to establish a thermal sensation prediction model, which accepts skin and air temperatures as input parameters. The model achieves a high prediction accuracy value of 0.84. Given the measurement errors of low-cost IR sensors, a simulated temperature measurement experiment based on the manikin was conducted, and an error calibration method based on multiple linear regression was developed through the experiment. 30 subjects participated in another prototype test experiment, which proved that the air conditioner can accomplish effective automatic control based on the prediction of thermal sensation, avoid the occurrence of extreme temperatures, and ensure the thermal comfort of personnel. The possible causes of remaining problems and potential improvement schemes are discussed in detail.

Anthropogenic influence on extremes and risk hotspots

Study of the frequency and magnitude of climate extremes as the world warms is of utmost importance, especially separating the influence of natural and anthropogenic forcing factors. Record-breaking temperature and precipitation events have been studied using event-attribution techniques. Here, we provide spatial and temporal observation-based analyses of the role of natural and anthropogenic factors, using state-of-the-art time series methods. We show that the risk from extreme temperature and rainfall events has severely increased for most regions worldwide. In some areas the probabilities of occurrence of extreme temperatures and precipitation have increased at least fivefold and twofold, respectively. Anthropogenic forcing has been the main driver of such increases and its effects amplify those of natural forcing. We also identify risk hotspots defined as regions for which increased risk of extreme events and high exposure in terms of either high Gross Domestic Product (GDP) or large population are both present. For the year 2018, increased anthropogenic forcings are mostly responsible for increased risk to extreme temperature/precipitation affecting 94%/72% of global population and 97%/76% of global GDP relative to the baseline period 1961–1990.

Frost hardiness of apple generative buds during dormancy

<p class="042abstractstekst">The success of apple production is influenced by frost damages. Occurrence of extreme temperatures is increasing worldwide because of global warming, so the risk of frost damages is also increasing in apple orchards during dormancy and blooming time. In our work the frost hardiness of flower buds of eight apple cultivars was observed with artificial freezing tests during four subsequent dormancy periods in Hungary. The studied cultivar assortment contained two standard commercial cultivars (‘Gala’, ‘Idared’), two scab-resistant cultivars from abroad breeding programmes (‘Florina’, ‘Prima’) and four new Hungarian multi-resistant (mainly scab-resistant) cultivars (‘Artemisz’, ‘Cordelia’, ‘Hesztia’, ‘Rosmerta’). There were remarkable differences between cultivars and years from the aspect of frost hardiness of generative overwintering organs. At the end of hardening period, in January, the LT<sub>50</sub> values of flower buds were between -22.4 °C and -30.4 °C according to cultivar and year. LT<sub>50</sub> means the temperature causing 50 % frost damage in the flower buds of the certain cultivar in the certain time. ‘Gala’ and ‘Florina’ were the most frost hardy, while ‘Prima’, ‘Cordelia’ and ‘Idared’ the most sensitive to frost. Cold hardiness values of flower buds of ‘Artemisz’, ‘Rosmerta’ and ‘Hesztia’ cultivars were regularly between the values of two extreme groups. In winters with inappropriate weather the generative overwintering organs were unable to reach the genetically possible frost hardiness of them.</p>

Extreme Air Temperatures at the Southwestern Slope of Pirin Mountain (Bulgaria) and Related Synoptic Conditions


 
 
 The article examines the occurrence of extremely cold and extremely warm days as an indicator of environmental changes in the Pirin Mountain (Bulgaria). The study is based on hourly air temperature data recorded by an automatic weather station. By averaging the hourly data, daily values are calculated. The occurrence of extremely cold and extremely warm days during the period 2015–2018 for January, April, July and October are analyzed. Extreme temperature days are determined by threshold values for the 10th and 90th percentiles. Characteristic synoptic situations related to the manifestation of extreme temperatures are presented. As a result of the research it was established that the advections of air masses are the leading factor for the occurrence of extreme temperatures. In addition very important for the extremely low temperatures is the high air pressure over the Balkan Peninsula.
 
 

Climate change in the temperature and precipitation at two contrasting sites of the Argentinean wheat region

Global climate change is shifting temperature and precipitation regimes, which is modifying the environments that define wheat yield and quality. The current work characterises the changes that have occurred in the thermal and hydric environment in two contrasting sites of the wheat-growing region of Argentina, allowing comparison between sites for these changes and for how the changes are accelerating. Temperature and precipitation variables were analysed by regression and trend testing (Mann Kendall), and future projections were made based upon significant relationships. The two sites compared were in the zones around the cities of Azul in the Province of Buenos Aires and Marcos Juárez in the Province of Córdoba, located approximately 500 km apart. The climate data analysed covered the period 1931–2014 for Azul and 1952–2014 for Marcos Juárez. At both sites, temperatures increased significantly in mean and extreme values over these periods, where the rate of change accelerated during the first years of the twenty-first century. The changes observed were in general more pronounced in Marcos Juárez than in Azul. Furthermore, in Marcos Juárez, mean precipitation increased from September to December and there was a higher frequency of extremes of precipitation greater than 100 mm in September and October during the early twenty-first century. Evidence was found for temperature rise and the occurrence of extreme temperature and precipitation events occurring differently between sites, as well as for its acceleration rate in the early twenty-first century. The projected future changes made implied that wheat yield is expected to suffer losses over the coming century.

Variation in Extreme Temperature and Its Instability in China

In this study, the instability of extreme temperatures is defined as the degree of perturbation of the spatial and temporal distribution of extreme temperatures, which is to show the uncertainty of the intensity and occurrence of extreme temperatures in China. Based on identifying the extreme temperatures and by analyzing their variability, we refer to the entropy value in the entropy weight method to study the instability of extreme temperatures. The results show that TXx (annual maximum value of daily maximum temperature) and TNn (annual minimum value of daily minimum temperature) in China increased at 0.18 °C/10 year and 0.52 °C/10 year, respectively, from 1966 to 2015. The interannual data of TXx’ occurrence (CTXx) and TNn’ occurrence (CTNn), which are used to identify the timing of extreme temperatures, advance at 0.538 d/10 year and 1.02 d/10 year, respectively. In summary, extreme low-temperature changes are more sensitive to global warming. The results of extreme temperature instability show that the relative instability region of TXx is located in the middle and lower reaches of the Yangtze River basin, and the relative instability region of TNn is concentrated in the Yangtze River, Yellow River, Langtang River source area and parts of Tibet. The relative instability region of CTXx instability is distributed between 105° E and 120° E south of the 30° N latitude line, while the distribution of CTNn instability region is more scattered; the TXx’s instability intensity is higher than TNn’s, and CTXx’s instability intensity is higher than CTNn’s. We further investigate the factors affecting extreme climate instability. We also find that the increase in mean temperature and the change in the intensity of the El Niño phenomenon has significant effects on extreme temperature instability.

Risk assessment framework for Mediterranean airports: a focus on extreme temperatures and precipitations and sea level rise

The increase of frequency and severity of extreme weather events due to climate change gives evidence of severe challenges faced by infrastructure systems. Among them, the aviation sector is particularly at risk from the potential consequences of climate change. Airports are classified as critical infrastructures because they provide fundamental functions to sustain societies and economic activities. More specifically, Mediterranean airports face risks associated with sea level rise, higher occurrence of extreme temperature and precipitation events. These aspects require the implementation of appropriate risk assessments and definition of targeted adaptation strategies, which are still limited in the Mediterranean region. The aim of the present paper is to provide theoretical frameworks in order to assess risks of climate change on Mediterranean airports, related to extreme temperature, extreme precipitation and sea level rise. Starting from a review of the literature, we first identify the sources of climate risk that may induce potential impacts on airports, here divided in air side and land side components. In order to do so, we select a series of indicators used as proxies for identifying hazard, exposure and vulnerability. The application of these theoretical frameworks allows defining the level of risk associated to each hazard, with the goal to support the identification of specific adaptation measures for the Mediterranean airports.

Adaptive metabolic responses in a thermostabilized environment: Transgenerational trade-off implications from tropical tilapia

Relatively warm environments caused by global warming enhance the productivity of aquaculture activities in tropical/subtropical regions; however, the intermittent cold stress (ICS) caused by negative Arctic Oscillation can still result in major economic losses. In contrast to endotherms, ectothermic fishes experience ambient temperature as an abiotic factor that is central to performance and survival. Therefore, the occurrence of extreme temperatures caused by climate change has ignited a surge of scientific interest from ecologists, economists and physiologists. In this study, we test the transgenerational effects of rearing cold-experienced (CE) and cold-naïve (CN) strains of tropical tilapia. Our results show that compared to CN tilapia, the CE strain preferentially converts carbohydrates into lipids in liver at a regular temperature of 27 °C. Besides, at a low temperature of 22 °C, the CE strain exhibits a broader aerobic scope than CN fish, and their metabolite profile suggests a metabolic shift towards the utilization of glutamate derivatives. Therefore, in response to thermal perturbations, this transgenerational metabolic adjustment provides evidence into the adaptive trade-off mechanisms in tropical fish. Nevertheless, global warming may result in less thermal variation each year, and the stabilized ambient temperature may cause tropical tilapia to gradually exhibit lower energy deposits in liver. In addition to those habitants in cold and temperate regions, a lack of cold exposure to multiple generations of fish may decrease the native cold-tolerance traits of subtropical/tropical organisms; this notion has not been previously explored in terms of the biological effects under anthropogenic climate change.

Forecast Models for Urban Dynamics (Kathmandu Valley)

Forecast Models for Urban Dynamics (Kathmandu Valley)

Estimating the Extreme Temperature Occurrence Over Pakistan Using Interannual and Interdecadal Temperature Variation and Teleconnections During 1901-2018

Estimating the Extreme Temperature Occurrence Over Pakistan Using Interannual and Interdecadal Temperature Variation and Teleconnections During 1901-2018

An integrated package to evaluate climatic suitability for agriculture

We developed a tool in R language called agroclim to compute climatic indices related to agriculture development. Using a wide collection of functions and based on daily temperature series, the users are able to: 1) assess the general suitability of specific locations; 2) check the probability of occurrence of extreme temperatures; and 3) evaluate the climatic suitability for specific crops. These features are based on the use of agroclimatic indicators, trying to capture the effect of climate in crops, from sowing to maturity. Furthermore, a group of functions is dedicated to handle multidimensional datasets to i) retrieve climatic data; ii) compute the indices over spatially-continuous datasets; and iii) export results in a 3-dimension format. Using a high-resolution temperature dataset of Spain as an input exemplar, we computed the complete set of agroclimatic indices to selected locations and mapped several of them to show their potential for spatial analysis of climatic suitability.

Extreme Temperature Events during 1960–2017 in the Arid Region of Northwest China: Spatiotemporal Dynamics and Associated Large-Scale Atmospheric Circulation

Studying the dynamic changes of extreme temperatures and associated large-scale atmospheric circulation is important for predicting the occurrence of extreme temperatures and reducing their adverse impact and damage. Based on the surface temperature data sets collected from 87 weather stations over the arid region of Northwest China (ARNC) during 1960–2017, the Sen’s slope estimator, Mann–Kendall test, Cumulative anomaly, Moving t-test, and Synthetic analysis methods were used to analyze the spatiotemporal dynamics and breaking-point change characteristics of extreme temperatures, and to discuss its associated large-scale atmospheric circulation. The results revealed that at the temporal scale, summer days (SU25), warm days (TX90p), warm nights (TN90p), and warm spell duration indicator (WSDI) showed a remarkable increasing trend at the rates of 2.27, 1.49, 3, and 2.28 days/decade, respectively. The frost days (FD), cold days (TX10p), cold nights (TN10p), and cold spell duration indicator (CSDI) significantly decreased at the rates of −3.71, −0.86, −1.77, and −0.76 days/decade, respectively, during the study period. Spatially, the warming trend in the study area is very obvious as a whole, despite pronounced spatial differences in warming rate. After the breakpoint years, the frequency and probability distribution for extreme warm and cold indices were all inclined to the hotter part of the density distribution. This indicates that the climate over the study region shifted sharply and tended to be warmer. The analysis of large-scale atmospheric circulation indicates that the warming trend in the arid region of Northwest China (ARNC) is positively correlated with geopotential height at 500 hPa and negatively correlated with total cloudiness. The findings from this study have important implications for forecasting extreme temperature events and mitigating the impacts of climatological disasters in this region.

Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America

A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes.

Bayesian time‐varying quantile regression to extremes

AbstractMaximum analysis consists of modeling the maximums of a data set by considering a specific distribution. Extreme value theory (EVT) shows that, for a sufficiently large block size, the maxima distribution is approximated by the generalized extreme value (GEV) distribution. Under EVT, it is important to observe the high quantiles of the distribution. In this sense, quantile regression techniques fit the data analysis of maxima by using the GEV distribution. In this context, this work presents the quantile regression extension for the GEV distribution. In addition, a time‐varying quantile regression model is presented, and the important properties of this approach are displayed. The parameter estimation of these new models is carried out under the Bayesian paradigm. The results of the temperature data and river quota application show the advantage of using this model, which allows us to estimate directly the quantiles as a function of the covariates. This shows which of them influences the occurrence of extreme temperature and the magnitude of this influence.

Temperature extremes in Alaska: temporal variability and circulation background

The aims of this study are to characterize the spatial and temporal variability of extremely warm days (WDs) and warm spells (WSs) in summer as well as extremely cold days (CDs) and cold spells (CSs) in winter in Alaska in the years 1951–2015 and to determine the role of atmospheric circulation in their occurrence. The analysis is performed using daily temperature maxima (TMAX) and minima (TMIN) measured at 10 weather stations in Alaska as well as mean daily values of sea level pressure and wind direction at the 850 hPa isobaric level.WD (CD) is defined as a day with TMAX above the 95th (TMIN below the 5th) percentile of a probability density function calculated from observations, and WS (CS) equals at least three consecutive WDs (CDs). Frequency of the occurrence and severity of warm and cold extremes as well as duration of WSs and CSs is analyzed. In order to characterize synoptic conditions during temperature extremes, the objective classification scheme of advection types considering jointly the direction of the air influx and type of pressure system is employed.The results show that the general trend is towards the warmer temperatures, and the warming is greater in the winter than summer and for TMAX as opposed to TMIN. This is reflected in changes in the frequency of occurrence and intensity of temperature extremes which are much more pronounced in the case of winter cold extremes (decreasing tendencies) than summer warm extremes (increasing tendencies).The occurrence of temperature extremes is generally favored by anticyclonic weather with advection direction indicating air mass flows from the interior of the North American continent as well as the south (warm extremes in summer) and north (cold extremes in winter).

Analysis of long-term changes in extreme climatic indices: a case study of the Mediterranean climate, Marmara Region, Turkey

This study aimed to analyze extreme temperature and precipitation indices at seven stations in the Marmara Region of Turkey for the period 1961–2016. The trend of temperature indices showed that the warm-spell duration and the numbers of summer days, tropical nights, warm nights, and warm days have increased, while the cold-spell duration and number of ice days, cool nights, and cool days have decreased across the Marmara Region. Additionally, the diurnal temperature range has slightly increased at most of the stations. A majority of stations have shown significant warming trends for warm days and warm nights throughout the study area, whereas warm extremes and night-time based temperature indices have shown stronger trends compared to cold extremes and day-time indices. The analysis of precipitation indices has mostly shown increasing trends in consecutive dry days and increasing trends in annual rainfall, rainfall intensity for inland and urban stations, especially for stations in Sariyer and Edirne, which are affected by a fast rate of urbanization. Overall, a large proportion of study stations have experienced an increase in annual precipitation and heavy precipitation events, although there was a low percentage of results that was significant. Therefore, it is expected that the rainfall events will tend to become shorter and more intense, the occurrence of temperature extremes will become more pronounced in favor of hotter events, and there will be an increase in the atmospheric moisture content over the Marmara Region. This provides regional evidence for the importance of ongoing research on climate change.