Explore the vast range of titles available.

Pakistan is one of the most vulnerable countries of the world to temperature extremes due to its predominant arid climate and geographic location in the fast temperature rising zone. Spatial distribution of the trends in annual and seasonal temperatures and temperature extremes over Pakistan has been assessed in this study. The gauge-based gridded daily temperature data of Berkeley Earth Surface Temperature (BEST) having a spatial resolution of 1° × 1° was used for the assessment of trends over the period 1960–2013 using modified Mann-Kendall test (MMK), which can discriminate the multi-decadal oscillatory variations from secular trends. The results show an increase in the annual average of daily maximum and minimum temperatures in 92 and 99% area of Pakistan respectively at 95% level of confidence. The annual temperature is increasing faster in southern high-temperature region compared to other parts of the country. The minimum temperature is rising faster (0.17–0.37 °C/decade) compared to maximum temperature (0.17–0.29 °C/decade) and therefore declination of diurnal temperature range (DTR) (− 0.15 to − 0.08 °C/decade) in some regions. The annual numbers of both hot and cold days are increasing in whole Pakistan except in the northern sub-Himalayan region. Heat waves are on the rise, especially in the hot Sindh plains and the Southern coastal region, while the cold waves are becoming lesser in the northern cold region. Obtained results contradict with the findings of previous studies on temperature trends, which indicate the need for reassessment of climatic trends in Pakistan using the MMK test to understand the anthropogenic impacts of climate change.

Previous observational analyses show that the land-surface diurnal temperature range (DTR) has decreased in the past 6 decades worldwide. Based on a newly developed China Meteorological Administration–Land Surface Air Temperature (CMA-LSAT) dataset, we analyzed the DTR changes between 1901 and 2014. Results indicate that the global land surface DTR significantly decreased at a rate of − 0.036 °C decade− 1 over the 1901–2014 period, mainly due to the large decrease in DTR from 1951 to 2014. For the first half of the twentieth century, most grid boxes (spatial resolution 5° × 5°) show a positive DTR trend, with the positive trends of 32.4% grid boxes being statistically significant, leading to a large and significant increase of 0.048 °C decade− 1 in DTR. However, a dramatic reversal in DTR change occurred in early 1950s, with most parts of global lands exhibiting a shift from increasing to decreasing trends. The global land average DTR decrease during 1951–2014 was − 0.054 °C decade− 1, with 45.0% grid boxes showing significant negative trends. The reverse phenomenon is more obvious in the Northern Hemisphere than that in the Southern Hemisphere. For the periods 1979–2014 and 1998–2014, the decreasing trends in DTR mainly occur in the Northern Hemisphere. The DTR in the Southern Hemisphere experienced much larger increases during the two recent periods than during the period 1951–2014. Asia, Eastern North America, and Australia exhibited widespread decreases in DTR, although the trend pattern for global DTR is generally mixed during 1979–2014 and 1998–2014. There is a good negative correlation between DTR and precipitation in the Northern Hemisphere from 1901 to 2014, with a correlation coefficient of − 0.61. The change in precipitation and number of volcanic eruptions, and the “early brightening” of Europe (Stockholm) all benefit the increase of DTR at global and regional scales in the first half of the twentieth century.

Dengue is a mosquito-borne viral disease that occurs mainly in the tropics and subtropics but has a high potential to spread to new areas. Dengue infections are climate sensitive, so it is important to better understand how changing climate factors affect the potential for geographic spread and future dengue epidemics. Vectorial capacity (VC) describes a vector's propensity to transmit dengue taking into account human, virus, and vector interactions. VC is highly temperature dependent, but most dengue models only take mean temperature values into account. Recent evidence shows that diurnal temperature range (DTR) plays an important role in influencing the behavior of the primary dengue vector Aedes aegypti. In this study, we used relative VC to estimate dengue epidemic potential (DEP) based on the temperature and DTR dependence of the parameters of A. aegypti. We found a strong temperature dependence of DEP; it peaked at a mean temperature of 29.3°C when DTR was 0°C and at 20°C when DTR was 20°C. Increasing average temperatures up to 29°C led to an increased DEP, but temperatures above 29°C reduced DEP. In tropical areas where the mean temperatures are close to 29°C, a small DTR increased DEP while a large DTR reduced it. In cold to temperate or extremely hot climates where the mean temperatures are far from 29°C, increasing DTR was associated with increasing DEP. Incorporating these findings using historical and predicted temperature and DTR over a two hundred year period (1901-2099), we found an increasing trend of global DEP in temperate regions. Small increases in DEP were observed over the last 100 years and large increases are expected by the end of this century in temperate Northern Hemisphere regions using climate change projections. These findings illustrate the importance of including DTR when mapping DEP based on VC.

The presence of short- and long-term autocorrelations can lead to considerable change in significance of trend in hydro-climatic time series. Therefore, past findings of climatic trend studies that did not consider autocorrelations became a questionable issue. The spatial patterns in the trends of annual and seasonal temperature, rainfall, and related extremes in Egypt have been assessed in this paper using modified Mann-Kendal (MMK) trend test which can detect unidirectional trends in time series in the presence of short- and long-term autocorrelations. The trends obtained using the MMK test was compared with that obtained using standard Mann-Kendall (MK) test to show how natural variability in climate affects the trends. The daily rainfall and temperature data of Princeton Global Meteorological Forcing for the period 1948–2010 having a spatial resolution of 0.25° × 0.25° was used for this purpose. The results showed a large difference between the trends obtained using MMK and MK tests. The MMK test showed increasing trends in temperature and a number of temperature extremes in Egypt, but almost no change in rainfall and rainfall extremes. The minimum temperature was found to increase (0.08–0.29 °C/decade) much faster compared to maximum temperature (0.07–0.24 °C/decade) and therefore, a decrease in diurnal temperature range (− 0.01 to − 0.16 °C/decade) in most part of Egypt. The number of winter hot days and nights are increasing, while the number of cold days is decreasing in most part of the country. The study provides a more realistic scenario of the changes in climate and weather extremes of Egypt.

As climate change intensifies, ambient temperatures have become a global concern, leading to an increasing number of studies examining the impact of temperature on human health. Extreme weather events, including heatwaves and cold spells, are becoming more frequent and severe. Numerous studies have highlighted the positive correlation between non-optimal ambient temperatures and mortality. Understanding these impacts is crucial for developing targeted public health interventions and accurately predicting the future health burden associated with climate variability. This study aims to estimate the relative risks and mortality burden associated with temperature extremes over the past decade, focusing on the contributions of both heat and cold, as well as mild and extreme temperatures, and identifying vulnerable populations. By doing so, filling a regional research gap in Guiyang. We collected the daily weather and mortality data from 2013 to 2023. Descriptive analysis was conducted to characterize overall weather patterns and mortality trends during the study period. A quasi-Poisson regression with a distributed lag non-linear model (DLNM), incorporating a 21-day lag and controlling for trends, air pollutants, and the day of the week, was applied to estimate the cumulative relative risks of non-accidental mortality due to non-optimal and extreme temperatures. We calculated attributable fractions and attributable numbers for heat and cold (defined as temperatures above and below the daily mean temperature), mild temperatures (defined using cutoffs at the minimum mortality temperature, with mild heat ranging from the minimum mortality temperature to the 97.5th temperature percentile and mild cold ranging from the 2.5th temperature percentile to the minimum mortality temperature) and extreme temperatures (defined as temperatures below the 2.5th temperature percentile for extreme cold and above the 97.5th temperature percentile for extreme heat). A total of 140,099 non-accidental deaths were included in the study.Temperature and mortality showed U-shaped associations, except for 0-64 years age group. For extreme low temperatures, the effects appeared in lag 2 to 4 days and lasted for approximately 18 days, peaking on lag day 5, yielding a cumulative relative risks (RRs) of 1.24% (95% CI 1.14% to 1.36%) for non-accidental mortality. For extreme high temperatures, the strongest effect was observed on the same day, with an RR of 1.18%(95% CI 1.03% to 1.35%). The attributable fraction of non-accidental mortality associated with non-optimal temperatures was 9.21% (95% eCI: 5.32% to 12.15%). The mortality burden from heat and cold was 5.55% (95% eCI: 2.04% to 8.59%) and 3.67% (95% eCI: 1.45% to 5.80%), respectively. Mild heat was responsible for the majority of the mortality burden. Extreme low temperatures had higher cumulative relative risk and a prolonged effect compared to extreme high temperatures. The attributable fraction associated with non-optimal temperatures was highest for respiratory-related deaths. Mild heat was responsible for the majority of the mortality burden. Additionally, males and the individuals aged 65 years and above were particularly vulnerable populations.

In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information.

Heat waves are becoming more intense and extreme as a consequence of global warming. Epidemiological evidence reveals the health impacts of heat waves in mortality and morbidity outcomes, however, few studies have been conducted in tropical regions, which are characterized by high population density, low income and low health resources, and susceptible to the impacts of extreme heat on health. The aim of this paper is to estimate the effects of heat waves on cardiovascular and respiratory mortality in the city of Rio de Janeiro, Brazil, according to sex, age, and heat wave intensity. We carried out a time-stratified case-crossover study stratified by sex, age (0-64 and 65 or above), and by sex for the older group. Our analyses were restricted to the hot season. We included 42,926 participants, 29,442 of whom died from cardiovascular and 13,484 from respiratory disease, between 2012 and 2017. The death data were obtained from Rio de Janeiro's Municipal Health Department. We estimated individual-level exposure using the inverse distance weighted (IDW) method, with temperature and humidity data from 13 and 12 stations, respectively. We used five definitions of heat waves, based on temperature thresholds (90th, 92.5th, 95th, 97.5th, and 99th of individual daily mean temperature in the hot season over the study period) and a duration of two or more days. Conditional logistic regression combined with distributed lag non-linear models (DLNM) were used to estimate the short-term and delayed effects of heat waves on mortality over a lag period (5 days for cardiovascular and 10 for respiratory mortality). The models were controlled for daily mean absolute humidity and public holidays. The odds ratios (OR) increase as heat waves intensify, although some effect estimates are not statistically significant at 95% level when we applied the most stringent heat wave criteria. Although not statistically different, our central estimates suggest that the effects were greater for respiratory than cardiovascular mortality. Results stratified by sex and age were also not statistically different, but suggest that older people and women were more vulnerable to the effects of heat waves, although for some heat wave definitions, the OR for respiratory mortality were higher among the younger group. The results also indicate that older women are the most vulnerable to heat wave-related cardiovascular mortality. Our results show an increase in the risk of cardiovascular and respiratory mortality on heat wave days compared to non-heat wave ones. These effects increase with heat wave intensity, and evidence suggests that they were greater for respiratory mortality than cardiovascular mortality. Furthermore, the results also suggest that women and the elderly constitute the groups most vulnerable to heat waves.

This paper describes a publicly available, long-term (1915–2011), hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of 1 16 0 latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset has 1) extended the period of analysis (1915–2011 versus 1950–2000), 2) increased the spatial resolution from ⅛° to 1 16 0 , and 3) used an updated version of VIC. The previous dataset has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. It is anticipated that the spatial refinement and temporal extension will be of interest to a wide cross section of the scientific community.

Guided by the Paris Agreement, the IPCC Special Report on Global Warming of 1.5 °C reported potential risks of climate change at different global warming levels (GWLs). To provide fundamental information on future temperature extremes over Southeast Asia (SEA), projected changes in temperature extreme indices are evaluated for different seasons at 1.5 °C and 2 °C GWLs against the historical reference period of 1976–2005 based on the ensemble of CORDEX simulations. Results show that the temperature indices increase significantly across the Indochina Peninsula and Maritime Continent at both GWLs except for decreasing daily temperature range (DTR) in the dry season, with more pronounced magnitudes at 2 °C GWL. Moreover, the regionally averaged ensemble medians of the indices show various changes over different subregions. At 1.5 °C and 2 °C GWLs, most pronounced increases of threshold indices. i.e. summer days (SU) and tropical nights (TR), are projected in Sumatra and Sulawesi for both wet and dry seasons. The warm spell duration (WSDI) increases generally, with strongest magnitudes for Sumatra and Sulawesi (Philippines and Sulawesi) in the wet (dry) season. On the other hand, significant increases of warm days and nights can also be observed at 2 °C GWL compared to 1.5 °C, particularly in the dry season, suggesting the high sensitivity of temperature extremes over the SEA. The projected potentially conspicuous temperature extremes under global warming of 1.5 °C and 2 °C primarily concentrate on the densely populated coastal regions of the main islands, showing the necessity of restricting global warming to 1.5 °C aiming at the eradication and reduction of regional climate stress for the human system in the developing countries over the SEA.

We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016. We find that cold temperatures, hot temperatures, precipitation, narrower daily temperature ranges, humidity, and cloud cover are all associated with worsened expressions of sentiment, even when excluding weather-related posts. We compare the magnitude of our estimates with the effect sizes associated with notable historical events occurring within our data.